Günümüzde kullanım amacına göre buğdayın tane verimi yanında bazı kalite özelliklerinin de belirlenmesi gerekmektedir. Bu çalışma, Yozgat ekolojik koşullarında 14 farklı ekmeklik buğday çeşidinin verim ve kalite yönünden durumlarını belirlemek amacıyla 2010-2014 yılları arasında yürütülmüştür. Deneme tesadüf blokları deneme desenine göre üç tekrarlamalı olarak gerçekleştirilmiştir. Araştırmada çeşitlerin bitki boyu, tane verimi, bin tane ağırlığı, hektolitre ağırlığı, kül oranı, yağ oranı, protein içeriği, nişasta içeriği, yaş gluten içeriği, Zeleny sedimentasyon değeri, asit deterjanda çözünmeyen lif (ADF) ve nötr deterjanda çözünmeyen lif (NDF) değerleri incelenmiştir. Üç yılın ortalamasına göre çeşitlerin bitki boyları 60.2–80.3 cm, tane verimleri 290.5-372.2 kg da -1 , bin tane ağırlıkları 29.2–38.4 g, hektolitre ağırlıkları 77.7–79.7 kg, kül oranları % 1.62-1.82, yağ oranları % 1.40-1.91, protein oranları % 12.0–13.8, nişasta içeriği % 61.6-65.0, yaş gluten içeriği % 23.9-28.0, sedimentasyon değerleri 21.5 – 33.1 ml, ADF ve NDF değerleri sırasıyla % 3.5-4.4 ile %14.9-16.0 arasında değişmiştir. En yüksek tane verimi Tosunbey (372.2 kg da -1 ), Flamura 85 (367.1 kg da -1 ), Syrena Odes'ka (357.0 kg da -1 ) çeşitlerinden elde edilmiştir. Bununla birlikte, Biplot analiz grafiğine göre de Tosunbey ve Syrena Odes’ka çeşitleri hektolitre ağırlığı, bin tane ağırlığı, nişasta ve yağ oranı ile bitki boyu bakımından öne çıkmışlardır.

Soil salinization poses a significant challenge to the sustainability and productivity of agriculture worldwide. This issue continues to hinder plant growth, requiring innovative solutions to alleviate salt stress. Moreover, climate change accelerates soil salinization, which may soon spread to previously unaffected agricultural areas. Therefore, the present study evaluated the potential role of different seed priming agents (hydro (H), salicylic acid (SA), proline (P), and melatonin (MEL)) on seedlings and leaf macro and micronutrients of sorghum grown under four (.27, 2.5, 5.0, and 8.0 dS m⁻¹) soil salinity conditions. Soil salinity drastically reduced all the growth parameters of sorghum seedlings, primarily the reduction in growth traits, which was remarkable after 2.5 dS m⁻¹ soil salinity. In addition, plant height, shoot fresh weight, and stomata were reduced by 40.8%, 74.6%, and 36.5%, respectively, at 8.0 dS m⁻¹ compared to .27 dS m⁻¹. SA‐ and MEL‐primed seeds mitigated the harmful effects of soil salinity by reducing Na⁺ accumulation in the leaves and increasing the K⁺/Na⁺ and Ca²⁺/Na⁺ ratios and photosynthetic activity under salt stress. However, the Zn²⁺, Mn²⁺, and Cu²⁺ contents of sorghum leaves increased with increasing soil salinity, and these nutrients also improved with seed priming by SA, MEL, and P. Considering all nutrients, MEL‐primed sorghum seeds had better macro‐ and micro‐nutrient uptake capacities than the H, SA, and P treatments under high soil salinity conditions. Finally, the present study showed that MEL‐induced improvement in salt tolerance in sorghum seedlings was related to enhanced nutritional status, photosynthetic activity, and biomass production in salinized areas.

Accurate assessment of crop health and yield potential facilitates precise estimation of above-ground biomass (AGB). Traditional AGB estimation methods are often limited by their destructive, labor-intensive nature. This study developed a rapid, non-destructive approach to estimate sorghum AGB using high-resolution unmanned aerial vehicle (UAV) data and machine learning (ML). A two-year field experiment tested four irrigation strategies: full irrigation at 100% of crop evapotranspiration (S1), partial deficits at 75% and 50% of S1, and a rain-fed (S4). This gradient assessed the ML model’s robustness across diverse conditions, yielding 216 AGB measurements reflecting variable plant responses to water stress. Multispectral and canopy height data were derived from UAV imagery collected during the sorghum growing season. Three ML algorithms—Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN)—were applied. RF outperformed others with an R² of 0.80, RMSE of 0.78 kg m⁻², and MAE of 0.58 kg m⁻², followed by SVM (R² = 0.64, RMSE = 1.08 kg m⁻², MAE = 0.77 kg m⁻²), while K-NN showed the lowest accuracy (R² = 0.50, RMSE = 1.26 kg m⁻², MAE = 0.96 kg m⁻²). Optimal RF hyperparameters were identified, and estimated AGB aligned closely with ground measurements, showing no significant differences. Spatial AGB maps effectively highlighted variability across treatments. This study demonstrates that UAV-based remote sensing combined with ML offers a reliable, non-destructive method for sorghum AGB estimation, enhancing precision agriculture applications such as irrigation management, crop monitoring, and yield prediction.

Abiotic stress factors encountered in production lands influence both the yield and the quality traits of bread wheat. This study investigated the effects of three different water table depths (30, 55, and 80 cm) and four different groundwater salinity levels (0.38, 2.0, 4.0, and 8.0 dSm⁻¹) on some quality traits of bread wheat under irrigated and unirrigated conditions. The experiments were conducted in the 2018 and 2019 growing seasons in randomized blocks—factorial (three factors) experimental design with three replications under controlled conditions. The hectoliter weight, fat ratio, starch ratio, protein content, Zeleny sedimentation, wet gluten content, ash ratio, acid detergent fiber (ADF), and neutral detergent fiber (NDF) values were investigated. The hectoliter weights varied between 66.1% and 77.8 kg, fat ratios between 1.49% and 1.70%, starch ratios between 61.9% and 67.8%, protein contents between 11.9% and 13.8%, Zeleny sedimentation values between 23.5 and 28.0 mL, wet gluten contents between 25.0% and 28.8%, ash ratios between 1.43% and 1.75%, and ADF values between 2.85% and 4.12%. The quality traits were positively influenced by increasing the water table depths. With increasing the groundwater salinity levels, the hectoliter weight, fat ratio, starch ratio, and NDF values decreased, while the protein ratio, sedimentation value, wet gluten content, ash ratio, and ADF values increased.