The combination of high or low temperatures and high salt may cause significant harm to the yield, quality, and overall productivity of forage pea crops. The germination process, a crucial phase in the life cycle of forage peas, may be greatly influenced by varying temperature and salinity conditions. To comprehend the influence of these elements on the germination of forage peas, one must use many tactics, including the choice of resilient forage pea cultivars. The experiment aimed to evaluate the response of four forage pea cultivars (Arda, Ozkaynak, Taskent, and Tore) caused by various temperature (10 °C, 15 °C, and 20 °C) and salt (0, 5, 10, 15, and 20 dS m⁻¹) conditions at the germination stage using multivariate analysis and machine learning methods. An observation of statistical significance (p < 0.01) was made regarding the variations between genotypes, temperature–salt levels, and the interaction of the observed factors: germination percentage (GP), shoot length (SL), root length (RL), fresh weight (FW), and dry weight (DW). The cultivar Tore had the best values for SL (1.63 cm), RL (5.38 cm), FW (1.10 g), and DW (0.13 g) among all the cultivars. On the other hand, the Ozkaynak cultivar had the highest value for GP (89.13%). The values of all of the parameters that were investigated decreased as the salt level rose, whereas the values increased when the temperature level increased. As a result, the Tore cultivar exhibited the highest values for shoot length, root length, fresh weight, and dry weight variables when exposed to a maximum temperature of 20 °C and a saline level of 0 dS m⁻¹. It was determined that temperature treatment of fodder peas can reduce salt stress if kept at optimum levels. The effects of temperature and salt treatments on the germination data of several fodder pea cultivars were analyzed and predicted. Three distinct machine learning algorithms were used to create predictions. Based on R² (0.899), MSE (5.344), MAPE (6.953), and MAD (4.125) measures, the MARS model predicted germination power (GP) better. The GPC model performed better in predicting shoot length (R² = 0.922, MSE = 0.602, MAPE = 11.850, and MAD = 0.326) and root length (R² = 0.900, MSE = 0.719, MAPE = 12.673, and MAD = 0.554), whereas the Xgboost model performed better in estimating fresh weight (R² = 0.966, MSE = 0.130, MAPE = 11.635, and MAD = 0.090) and dry weight (R² = 0.895, MSE = 0.021, MAPE = 12.395, and MAD = 0.013). The results of the research show that the techniques and analyses used can estimate stress tolerance, susceptibility levels, and other plant parameters, making it a cost-effective and reliable way to quickly and accurately study forage peas and related species.

It is known that salinity is one of the most important abiotic stress factors reducing productivity in agriculture. High salt concentration has a negative effect on seed germination and seedling formation. Magnetic field treatment can be considered as an environmentally friendly, innovative and inexpensive method to increase tolerance to stress, as a productivity-enhancing factor in agriculture. In this study, germination rate, germination strength, shoot and root length, fresh and dry weights, chlorophyll a, b and total chlorophyll content and ion leakage values of three different barley varieties under the influence of 150 mT constant magnetic field at different time intervals were determined. In light of the results, it was determined that although it varies according to the barley varieties, it has a significant positive effect on the germination rate and strength depending on the duration of the applied magnetic field. This study revealed that the magnetic field seed treatment has different effects according to the treatment time and the severity of the salt stress.

Drought and temperature extremes are major abiotic stressors limiting legume productivity worldwide. This study investigates the germination and early seedling responses of six cultivars belonging to three Vicia species (V. sativa, V. pannonica, and V. narbonensis) under varying levels of polyethylene glycol (PEG)-induced drought and temperature conditions (12 °C, 18 °C, and 24 °C) in vitro. Significant cultivar-dependent differences were observed in the germination rate (GR), shoot and root length (SL and RL), fresh and dry weight (FW and DW), and vigor index (VI). The Ayaz cultivar exhibited superior performance, particularly under severe drought (10% PEG) and optimal temperature (24 °C), while Özgen and Balkan were most sensitive to stress. Principal component and correlation analyses revealed strong associations between the vigor index, shoot height, and fresh and dry weight, particularly in high-performing genotypes. To further model and predict stress responses, four machine learning (ML) algorithms—Random Forest (RF), k-Nearest Neighbors (k-NNs), Multilayer Perceptron (MLP), and Support Vector Machines (SVMs)—were employed. Based on model performance metrics, and considering high R² values along with low RMSE and MAE values, the MLP model demonstrated the most accurate predictions for the GR (R² = 0.95, RMSE = 0.06, MAE = 0.05) and VI (R² = 0.99, RMSE = 0.02, MAE = 0.01) parameters. In contrast, the RF model yielded the best results for the SL (R² = 0.98, RMSE = 0.02, MAE = 0.02) and DW (R² = 0.93, RMSE = 0.06, MAE = 0.04) parameters, while the highest prediction accuracy for the RL (R² = 0.83, RMSE = 0.09, MAE = 0.07) and FW (R² = 0.97, RMSE = 0.05, MAE = 0.03) parameters was achieved using the SVM model. Comparative analysis with recent studies confirmed the applicability of ML in stress physiology and genotype screening. This integrative approach offers a robust framework for genotype selection and stress tolerance modeling in legumes, contributing to developing climate-resilient crops.

Salinity is a critical environmental factor that adversely affects crop growth and productivity. This study aimed to evaluate the effects of gibberellic acid (GA3) on germination, shoot length, root length, and biomass of two wheat cultivars (Demirhan and Fazılbey) under varying levels of salt stress (0, 5, and 10 dS/m NaCl). GA3 was applied at concentrations of 100 and 200 ppm to assess its potential in alleviating the adverse effects of salinity. The results revealed that GA3 significantly improved germination percentage, shoot length, and root length in both cultivars, with the Demirhan cultivar showing more pronounced responses under higher salt stress. A biplot analysis highlighted the interaction between GA3 treatment, salinity levels, and cultivar performance, confirming the positive role of GA3 in mitigating the detrimental effects of salinity. These findings suggest that GA3 can be an effective strategy for enhancing wheat growth in saline soils. Further studies are recommended to optimize GA3 concentrations and evaluate its long-term effects under field conditions.

Bu çalışma; Kayseri ekolojik koşullarında macar fiği + arpa karışık ekim sisteminde uygun karışım oranlarını belirlemek amacı ile, 2015-2017 yıllarında Beta macar fiği ve Bülbül-98 arpa çeşidi kullanılarak tesadüf blokları deneme desenine göre 4 tekerrürlü olarak yürütülmüştür. Denemede, saf ekimler ile birlikte % 70 arpa + % 30 macar fiği, % 50 arpa + % 50 macar fiği ve % 30 arpa + % 70 macar fiği karışımları yer almıştır. Araştırmada kuru madde verimi, kuru otta macar fiğ oranı, ham protein oranı ve verimi, asit deterjan lif (ADF) ve nötr deterjan lif (NDF) oranları ile nispi yem değeri (NYD) incelenmiştir. Çalışmada, elde edilen sonuçlara göre; karışımda macar fiğ oranı arttıkça kuru madde veriminin azaldığı ve en yüksek kuru madde veriminin arpa yalın ekiminden elde edildiği tespit edilmiştir. Ancak karışımda macar fiğ oranı arttıkça NDF oranı azalmış, protein oranını ise artmıştır. İki yıla ait verim ve kalite (ADF, NDF, NYD ve protein) parametreleri birlikte incelendiğinde %70 macar fiği+%30 arpa karışımları Kayseri ve benzer ekolojiler için önerilebilir.

Çalışmanın amacı farklı oralarda karıştırılan vitiver (V) ve soyanın (S) silaj fermentasyonu, besinsel özellikler ile gaz ve metan üretimi üzerine etkisini incelemektir. Bu amaçla vitiver ve soya bitkileri beş farklı oranda (100V%, 75%V+25%25S, 50%V+50S, 25%V+75%S ve %100S) karıştırılmıştır. İncelenen tüm özellikler üzerine karışımın etkisi istatistiksel olarak çok önemli bulunmuştur (p≤0.01).Araştırma sonuçlarına göre silaj karışımındaki soya miktarının artmasıyla kuru madde miktarı, ham protein ve ham yağ oranları artmış, ADF ve NDF oranları azalmıştır. Vitiver oranının artması pH seviyesinde düşüş, gaz ve metan üretiminde, metabolik enerji ve organik madde sindirim derecesinde, ham külde, laktik asit oranlarında artış sağlamıştır. Ancak vetiverin bir çim türü olmasına rağmen soya ile karışımda iyi bir silaj üretimi için yeterli kuru madde miktarına ve uygun pH seviyesine sahip olmadığı görülmüştür. Kökleri için yoğun yetiştirilen vitiverin toprak üstü aksamının yalın silaj olarak değerlendirilmesi veya yeni bitkilerle karışımının yapılması gerekmektedir.