Yield forecasting is a rational and scientific way of predicting future occurrences in agriculture—the level of production effects. Its main purpose is reducing the risk in the decision-making process affecting the yield in terms of quantity and quality. The aim of the following study was to generate a linear and non-linear model to forecast the tuber yield of three very early potato cultivars: Arielle, Riviera, and Viviana. In order to achieve the set goal of the study, data from the period 2010–2017 were collected, coming from official varietal experiments carried out in northern and northwestern Poland. The linear model has been created based on multiple linear regression analysis (MLR), while the non-linear model has been built using artificial neural networks (ANN). The created models can predict the yield of very early potato varieties on 20th June. Agronomic, phytophenological, and meteorological data were used to prepare the models, and the correctness of their operation was verified on the basis of separate sets of data not participating in the construction of the models. For the proper validation of the model, six forecast error metrics were used: i.e., global relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE). As a result of the conducted analyses, the forecast error results for most models did not exceed 15% of MAPE. The predictive neural model NY1 was characterized by better values of quality measures and ex post forecast errors than the regression model RY1.

Coatings that reduce the fat content of fried food are an alternate option to reach both health concerns and consumer demand. Mucilage of garden cress (Lepidium sativum) seed extract (MSE) was modified into an edible coating with or without ascorbic acid (AA) to coat fresh-cut potato strips during cold storage (5 °C and 95% RH for 12 days) and subsequent frying. Physical attributes such as color, weight loss, and texture of potato strips coated with MSE solutions with or without AA showed that coatings efficiently delayed browning, reduced weight loss, and maintained the texture during cold storage. Moreover, MSE with AA provided the most favorable results in terms of reduction in oil uptake. In addition, the total microbial count was lower for MSE-coated samples when compared to the control during the cold storage. MSE coating also performed well on sensory attributes, showing no off flavors or color changes. As a result, the edible coating of garden cress mucilage could be a promising application for extending shelf-life and reducing the oil uptake of fresh-cut potato strips.

Garlic is an important vegetable in terms of its economic value and also as a medicinal plant. In this study, chitosan (300 mM) and yeast extract (8 g/L) were used individually or in combination to improve the yields of garlic plants under drought conditions (i.e., 75% and 50% of the water they would normally receive from irrigation) for two seasons. Significant decreases in numbers of leaves per plant and plant height, plant dry weight, relative water content, and chlorophyll a and b concentrations were found in stressed garlic plants in both seasons. The greatest reductions in these characters were recorded in plants that received only 50% of the normal irrigation in both seasons. Levels of hydrogen peroxide, products of lipid peroxidation such as malondialdehyde, and superoxide, as well as percentages of electrolyte leakage, were elevated considerably and were signals of oxidative damage. The application of the yeast extract (8 g/L) or chitosan (300 mM) individually or in combination led to a remarkable increase in the most studied characters of the stressed garlic plants. The combination of yeast extract (8 g/L) plus chitosan (300 mM) led to increase plant height (44%), ascorbic acid levels (30.2%), and relative water content (36.8%), as well as the chlorophyll a (50.7%) and b concentrations (79%), regulated the proline content and levels of antioxidant enzymes in stressed garlic plants that received 75% of the normal irrigation, and this decreased the signs of oxidative stress (i.e., percentage of electrolyte leakage and levels of malondialdehyde, hydrogen peroxide, and superoxide).

This study investigated the relationships between the electrical and selected mechanical properties of soil. The analyses focused on comparing various modeling relationships under study methods that included machine learning methods. The input parameters of the models were apparent soil electrical conductivity and magnetic susceptibility measured at depths of 0.5 m and 1 m. Based on the models, shear stress and soil compaction were predicted. Neural network models outperformed support vector machines and multiple linear regression techniques. Exceptional models were developed using a multilayer perceptron neural network for shear stress (R = 0.680) and a function neural network for soil compaction measured at a depth of 0–0.5 m and 0.4–0.5 m (R = 0.812 and R = 0.846, respectively). Models of very low accuracy (R < 0.5) were produced by the multiple linear regression.

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.

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.