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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.
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.
Prediction of Protein Content in Pea (Pisum sativum L.) Seeds Using Artificial Neural Networks
Pea (Pisum sativum L.) is a legume valued mainly for its high seed protein content. The protein content of pea is characterized by a high lysine content and low allergenicity. This has made consumers appreciate peas increasingly in recent years, not only for their taste, but also for their nutritional value. An important element of pea cultivation is the ability to predict protein content, even before harvest. The aim of this research was to develop a linear and a non-linear model for predicting the percentage of protein content in pea seeds and to perform a comparative analysis of the effectiveness of these models. The analysis also focused on identifying the variables with the greatest impact on protein content. The research included the method of machine learning (artificial neural networks) and multiple linear regression (MLR). The input parameters of the models were weather, agronomic and phytophenological data from 2016–2020. The predictive properties of the models were verified using six ex-post forecast measures. The neural model (N1) outperformed the multiple regression (RS) model. The N1 model had an RMS error magnitude of 0.838, while the RS model obtained an average error value of 2.696. The MAPE error for the N1 and RS models was 2.721 and 8.852, respectively. The sensitivity analysis performed for the best neural network showed that the independent variables most influencing the protein content of pea seeds were the soil abundance of magnesium, potassium and phosphorus. The results presented in this work can be useful for the study of pea crop management. In addition, they can help preserve the country’s protein security.
Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study
Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality.
Integrative approaches to enhance reproductive resilience of crops for climate-proof agriculture
Physiological and Antioxidative Effects of Strontium Oxide Nanoparticles on Wheat
We explored the impact of strontium oxide nanoparticles (SrO-NPs), synthesized through a green method, on seedling growth of bread wheat in hydroponic systems. The wheat plants were exposed to SrO-NPs concentrations ranging from 0.5 mM to 8.0 mM. Various parameters, including shoot length (cm), shoot fresh weight (g), root number, root length (cm), root fresh weight (g), chlorophyll value (SPAD), cell membrane damage (%), hydrogen peroxide (H2O2) value (µmol/g), malondialdehyde (MDA) value (ng/µL), and enzymatic activities like ascorbate peroxidase (APX) activity (EU/g FW), peroxidase (POD) activity (EU/g FW), and superoxide dismutase (SOD) activity (U/g FW), were measured to assess the effects of SrO-NPs on the wheat plants in hydroponic conditions. The results showed that the SrO-NPs in different concentrations were significantly affected considering all traits. The highest values were obtained from the shoot length (20.77 cm; 0.5 mM), shoot fresh weight (0.184 g; 1 mM), root number (5.39; 8 mM), root length (19.69 cm; 0 mM), root fresh weight (0.142 g; 1 mM), SPAD (33.20; 4 mM), cell membrane damage (58.86%; 4 mM), H2O2 (829.95 µmol/g; 6 mM), MDA (0.66 ng/µl; 8 mM), APX (3.83 U/g FW; 6 mM), POD (70.27 U/g FW; 1.50 mM), and SOD (60.77 U/g FW; 8 mM). The data unequivocally supports the effectiveness of SrO-NPs application in promoting shoot and root development, chlorophyll levels, cellular tolerance, and the activation of enzymes in wheat plants.
Exploring Digital Innovations in Agriculture: A Pathway to Sustainable Food Production and Resource Management
Today’s agriculture faces numerous challenges due to climate change, a growing population and the need to increase food productivity [...]
Zinc Oxide Nanoparticles: An Influential Element in Alleviating Salt Stress in Quinoa (Chenopodium quinoa L. Cv Atlas)
Climate change has intensified abiotic stresses, notably salinity, detrimentally affecting crop yield. To counter these effects, nanomaterials have emerged as a promising tool to mitigate the adverse impacts on plant growth and development. Specifically, zinc oxide nanoparticles (ZnO-NPs) have demonstrated efficacy in facilitating a gradual release of zinc, thus enhancing its bioavailability to plants. With the goal of ensuring sustainable plant production, our aim was to examine how green-synthesized ZnO-NPs influence the seedling growth of quinoa (Chenopodium quinoa L. Cv Atlas) under conditions of salinity stress. To induce salt stress, solutions with three different NaCl concentrations (0, 100, and 200 mM) were prepared. Additionally, Zn and ZnO-NPs were administered at four different concentrations (0, 50, 100, and 200 ppm). In this study, plant height (cm), plant weight (g), plant diameter (mm), chlorophyll content (SPAD), K/Na value, Ca/Na value, antioxidant enzyme activities (SOD: EU g−1 leaf; CAT: EU g−1 leaf; POD: EU g−1 leaf), H2O2 (mmol kg−1), MDA (nmol g−1 DW), proline (µg g−1 FW), and sucrose (g L−1), content parameters were measured. XRD analysis confirmed the crystalline structure of ZnO nanoparticles with identified planes. Salinity stress significantly reduced plant metrics and altered ion ratios, while increasing oxidative stress indicators and osmolytes. Conversely, Zn and ZnO-NPs mitigated these effects, reducing oxidative damage and enhancing enzyme activities. This supports Zn’s role in limiting salinity uptake and improving physiological responses in quinoa seedlings, suggesting a promising strategy for enhancing crop resilience. Overall, this study underscores nanomaterials’ potential in sustainable agriculture and stress management.
Predictions and Estimations in Agricultural Production under a Changing Climate
In the 21st century, agriculture is facing numerous challenges [...]
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.
GT Biplot and Cluster Analysis of Barley (Hordeum vulgare L.) Germplasm from Various Geographical Regions Based on Agro-Morphological Traits
Barley, an ancient crop, was vital for early civilizations and has historically been served as food and beverage. Today, it plays a major role as feed for livestock. Breeding modern barley varieties for high yield and quality has created significant genetic erosion. This highlights the importance of tapping into genetic and genomic resources to develop new improved varieties that can overcome agricultural bottlenecks and increase barley yield. In the current study, 75 barley genotypes were evaluated for agro-morphological traits. The relationships among these traits were determined based on genotype by trait (GT) biplot analysis for two cropping years (2021 and 2022). This study was designed as a randomized complete block experiment with four replications. The variation among genotypes was found to be significant for all traits. The correlation coefficient and GT biplot revealed that grain yield (GY) was positively correlated with the number of grains per spike (NGS), the grain weight per spike (GW), and the thousand kernel weight (1000 KW). However, the test weight (TW) was negatively correlated with the heading date (HD). Hierarchical analysis produced five groups in the first year, four groups in the second year, and four groups over the average of two years. Genotypes by trait biplot analysis highlighted G25, G28, G61, G73, and G74 as promising high-yielding barley genotypes. This study demonstrated the effectiveness of the GT biplot as a valuable approach for identifying superior genotypes with contrasting traits. It is considered that this approach could be used to evaluate the barley genetic material in breeding programs.
Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms
Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach.
Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management
Combining machine learning algorithms with Earth observations has great potential in the context of crop monitoring and management, which is essential in the face of global challenges related to food security and climate change [...]
New Trends and Challenges in Precision and Digital Agriculture
Real change is needed in the agricultural sector to meet the challenges of the 21st century in terms of humanity’s food needs [...]
Challenges and Problems of Nature Conservation: A Case Study from Poland
The article aims to show the attitudes and views of Polish residents on the problem of preserving the natural environment from the perspective of their place of residence. The need for research in this area stems from the insufficient number of available studies on this very important issue given the global environmental challenges we are facing. The research gap noted relates particularly to the aspects of engagement in environmental measures, knowledge levels, and motivations for conservation efforts by local citizens. Environmentally and socially responsible behavior is part of the concept of sustainable development. Empirical research covered a sample of 500 adult residents of Poland using the CAWI technique. The results showed that the vast majority of respondents noticed numerous problems in preserving the natural environment in their place of residence. According to respondents, the way to reduce these problems is to increase care for green areas, promote renewable energy sources, and strive to reduce waste. Moreover, the research results show that respondents take initiatives to segregate waste, save energy, and apply the zero-waste concept. The main reason for taking action to solve environmental problems is to preserve the environment for our children and future generations. The results of these studies showed that for men, pro-environmental activities are more important than for women. These findings are valuable for policymakers, local authorities, and fellow citizens.