Search Results (168)

Black point (BP) and Fusarium-damaged kernels are common disorders affecting wheat grains worldwide. While the negative impact of Fusarium head blight (FHB) on yield and grain quality is well established, the biological significance of BP remains debated. This study evaluated the effects of BP on yield-related traits and seedling performance of winter wheat and compared them with the effects of FHB. Four winter wheat cultivars (Mercedes, Adina, Steffi, and LG Mocca) were examined under field and laboratory conditions. Fusarium infection was induced by artificial inoculation with Fusarium culmorum, whereas BP was assessed under natural field conditions using non-inoculated control plants. Fusarium infection significantly reduced thousand-grain weight (up to 46%) and grain number per ear (up to 35%). In contrast, BP was not associated with yield reduction. Grain with BP symptoms showed a 10–30% higher thousand-grain weight compared with BP-free grain. Seedlings originating from BP-affected seeds exhibited equal or improved biometric traits and a higher vigor index. Phytopathological analysis showed that Alternaria spp. dominated the endophytic mycoflora of both BP-affected and BP-free seeds. These results indicate that, under the conditions of this study, BP did not negatively affect wheat yield or seedling vigor and differed fundamentally from the damaging effects of FHB, highlighting the importance of distinguishing BP from Fusarium-related damage in wheat production. Full article

To address the bottlenecks of low water and fertilizer utilization efficiency and limited yield potential inherent in Henan Province’s traditional winter wheat cultivation model of “furrow irrigation + conventional row seeding”, this study delved into the synergistic regulatory mechanisms of drip irrigation combined with wide-row precision seeding. It focused on their effects on the physiological ecology and yield-quality traits of winter wheat. A two-factor experiment, encompassing “sowing method × irrigation method” will be carried out during the 2024–2025 wheat growing season, featuring four treatments: furrow irrigation + conventional row seeding (QT), drip irrigation + conventional row seeding (DT), furrow irrigation + wide-row precision seeding (QK), and drip irrigation + wide-row precision seeding (DK). Results reveal that wide-row precision seeding optimized the canopy structure, raising the leaf area index (LAI) at the heading stage by 20.19% compared to QT, thereby enhancing ventilation and light penetration and reducing plant competition. Drip irrigation, with its precise water delivery, boosted the net photosynthetic rate of the flag leaf 35 days after flowering by 62.99% relative to QT, stabilizing root water uptake and significantly delaying leaf senescence. The combined effect of the two treatments (DK treatment) synergistically improved the canopy structure and photosynthetic performance of winter wheat, prolonging the functional period of green leaves by 29.41%. It established a highly efficient photosynthetic cycle, marked by “high stomatal conductance-low intercellular CO₂ concentration-high net photosynthetic rate”. The peak net photosynthetic rate (P_n) 13 days post-flowering rose by 23.9% compared to QT. Moreover, while reducing total water consumption by 21.4%, it substantially increased water use efficiency (WUE) and irrigation water use efficiency (IWUE) by 43.2% and 14.2%, respectively, compared to the QT control. Ultimately, the DK treatment achieved a synergistic enhancement in both yield and quality: grain yield increased by 14.7% compared to QT, wet gluten content reached 35.5%, and total protein yield per unit area rose by 13.1%. This study demonstrates that coupling drip irrigation with wide-row precision seeding is an effective strategy for achieving water-saving, high-yield, and high-quality winter wheat cultivation in the Huang-Huai-Hai region. This is achieved through the synergistic optimization of canopy structure, enhanced photosynthetic efficiency, and improved WUE. These findings provide a mechanistic basis and a scalable agronomic solution for sustainable intensification of winter wheat production under water-limited conditions in major cereal-producing regions. Full article

Chlorophyll serves as a crucial indicator for crop growth monitoring and reflects the health status of crops. Hyperspectral remote sensing technology, leveraging its advantages of repeated observations and high-throughput analysis, provides an effective approach for non-destructive chlorophyll monitoring. However, determining the optimal spectral scale remains the primary bottleneck constraining the widespread application of hyperspectral remote sensing in crop chlorophyll estimation: excessively fine spectral scale readily introduces redundant information, leading to dramatically increased data dimensions and reduced computational efficiency; conversely, overly coarse spectral scale risks losing critical spectral features such as absorption peaks and reflection troughs, thereby compromising model accuracy. Therefore, establishing an appropriate spectral scale that effectively preserves spectral feature information while maintaining computational efficiency is crucial for enhancing the accuracy and practicality of chlorophyll remote sensing estimation. To address this, this study proposes a three-dimensional analytical framework integrating “spectral scale—machine learning algorithm—crop growth stage” to systematically solve the scale optimization problem. Ground-truth measurements and hyperspectral data from five growth stages of winter wheat in Fengyang County, Anhui Province, were collected. Spectral bands sensitive to chlorophyll were analyzed, and four modeling methods—Ridge Regression (RR), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Regression (SVR)—were employed to integrate data from different spectral scales with respective bandwidths of 2, 3, 5, 7, 10, 20, and 50 nanometers (nm). The results evaluated the response characteristics of raw band reflectance to chlorophyll values and its impact on machine learning-based chlorophyll estimation across different spectral scales. Results indicate: (1) Canopy spectra significantly correlated with winter wheat chlorophyll primarily reside in the red and red-edge bands; (2) For single-scale analysis, larger spectral scales (10, 20 nm) enhance monitoring accuracy compared to 1 nm high-resolution data, while medium and small scales (5, 7 nm) may degrade accuracy due to redundant noise introduction. (3) Integrating growth stages, spectral scales, and machine learning revealed optimal monitoring accuracy during the jointing and heading stages using 1–5 nm spectral scales combined with the KNN algorithm. For the booting, flowering, and grain filling stages, the highest accuracy was achieved using 20–50 nm spectral scales combined with either the KNN or RF algorithm. The results indicate that high-precision chlorophyll inversion for winter wheat does not rely on a single fixed model or scale, but rather on the dynamic adaptation of the “scale-model-growth stage” triad. The proposed systematic framework not only provides a theoretical basis for chlorophyll monitoring using multi-platform remote sensing data, but also offers methodological support for future crop-sensing sensor design and data processing strategy optimization. Full article

An unmanned aerial vehicle (UAV) multi-spectral system provides a monitoring platform to rapidly obtain crop spectral information that can reflect crop nitrogen status for the generation of dynamic variable-rate nitrogen (VRN). To improve the accuracy of VRN prescription maps, a method of generating VRN prescription maps on the basis of the vegetation index was proposed, and the effects of UAV flight time and altitude on VRN prescription maps were analyzed. The experimental site was located in Dacaozhuang, Hebei Province, China, and the experimental crop was winter wheat (Lunxuan 145). The flight altitudes of the UAV system were set to 50, 70 and 90 m. The flight times were set to 8:00 a.m., 11:00 a.m., 2:00 p.m. and 5:00 p.m. local time. The flight area was 1.18 ha with a 60° rotation angle under a three-span center pivot irrigation system with an overhang. UAV flight missions were executed during the jointing, heading, and grain filling phases of winter wheat. There were 90 management zones with pie shapes in total, which were composed of a 10° angle in the rotation direction and 4 sprinklers along the lateral direction. The vegetation indices (VIs) which are closely related to crop nutrient status were selected and used to generate distribution maps, which were superimposed with the management zones to generate VRN prescription maps. The results demonstrated that the red-edge soil adjusted vegetation index (RESAVI) was relatively more sensitive to the nitrogen status of winter wheat than the other VIs were. The RESAVI distributions were stable during periods with a solar elevation angle greater than 50° (11:00 a.m.–2:00 p.m. local time), and the VRN prescription maps were similar, with the overlap percentage of the same fertilization grade being greater than 80% and the relative error of the fertilization amount being less than 5%. Compared with that at 2:00 p.m., the overlap percentage of the same fertilization grade was 56.6% in both seasons at 8:00 a.m., whereas flights at 5:00 p.m. exhibited overlaps of 70.9% and 44.6% in the 2023 and 2024 seasons, respectively. Conversely, the flight altitude had little influence on the fertilizer amount and VRN prescription maps. The difference in the amount of fertilizer used was less than 3% at different flight altitudes. The required time is half of that for a 50 m flight when the flight altitude is 70 m and one third of that when the flight altitude is 90 m. Our study recommended operating the UAV multi-spectral system at solar elevation angles greater than 50° when generating VRN prescription maps of winter wheat, and the flight height can be adjusted according to the field area and the endurance time of the UAV. Full article

Phenology is a key factor influencing the accuracy of regional-scale winter wheat-yield estimation. This study proposes a yield-estimation modeling framework centered on phenological zoning. Based on the remote sensing monitoring results of the heading stage of winter wheat in the Huang-Huai-Hai region from 2016 to 2021, the KNN-Ward spatial constraint clustering method was adopted to divide the Huang-Huai-Hai region into four consecutive wheat phenological zones. The results indicate a consistent spatio-temporal gradient in the phenology of winter wheat across the Huang-Huai-Hai region, characterized by later development in the northern areas and earlier development in the southern areas. The median day of year (DOY) for the heading stage in each zone varies by approximately 4 to 5 days, demonstrating a high degree of interannual stability. Building upon the phenological zoning outcomes, a multi-source data-driven random forest model was developed for wheat-yield estimation by integrating remote sensing data and meteorological variables during the wheat grain filling stage. This model incorporates remote sensing vegetation indices, crop growth parameters, and climatic factors as key input variables. Results show that the phenological zoning strategy significantly improves model prediction performance. Compared with the non-zoning model (R² = 0.46, RRMSE = 13.02%), the phenological zone model shows strong performance under leave-one-year-out cross-validation, with R² ranging from 0.54 to 0.68 and RRMSE below 12.50%. The phenological zoning model also exhibits more uniform residuals and higher prediction stability than models based on non-zoning, traditional agricultural zoning, and provincial administrative zoning. These results confirm the effectiveness of phenology-based zoning for regional yield estimation and provide a reliable framework for fine-scale crop yield monitoring. The phenological zoning model also demonstrates superior residual uniformity and prediction stability compared with models based on non-zoning, traditional agricultural zoning, and provincial administrative zoning. These results confirm the effectiveness of the multi-factor-driven modeling framework based on crop phenological zoning for regional yield estimation, providing a robust methodological foundation for fine-scale yield monitoring at the regional level. Full article

Accurate estimation of winter wheat yield is essential for ensuring food security. Recent studies on winter wheat yield estimation based on deep learning methods rarely explore the interpretability of the model from the perspective of crop growth mechanism. In this study, a multiscale winter wheat yield estimation framework (called MultiScaleWheatNet model) was proposed, which was based on time series remote sensing data and further takes into account meteorological and soil factors that affect wheat growth. The model integrated multimodal data from different temporal and spatial scales, extracting growth characteristics specific to particular growth stage based on the growth pattern of wheat phenological phase. It focuses on enhancing model accuracy and interpretability from the perspective of crop growth mechanisms. The results showed that, compared to mainstream deep learning architectures, the MultiScaleWheatNet model had good estimation accuracy in both rain-fed and irrigated farmlands, with higher accuracy in rain-fed farmlands (R² = 0.86, RMSE = 0.15 t·ha⁻¹). At the county scale, the accuracy of the model in estimating winter wheat yield was stable across three years (from 2021 to 2023, R² ≥ 0.35, RMSE ≤ 0.73 t·ha⁻¹, nRMSE ≤ 20.4%). Model interpretability results showed that, taking all growth stages together, the remotely sensed indices had relatively high contribution to wheat yield, with roughly equal contributions from meteorological and soil variables. From the perspective of the growth stages, the contribution of LAI in remote sensing factors demonstrated greater stability throughout the growth stages, particularly during the jointing, heading-filling and milky maturity stage; the combined impact of meteorological factors exhibited a discernible temporal sequence, initially dominated by water availability and subsequently transitioning to temperature and sunlight in the middle and late stages; soil factors demonstrated a close correlation with soil pH and cation exchange capacity in the early and late stages, and with organic carbon content in the middle stage. By deeply combining remote sensing, meteorological and soil data, the framework not only achieves high accuracy in winter wheat yield estimation, but also effectively interprets the dynamic influence mechanism of remote sensing data on yield from the perspective of crop growth, providing a scientific basis for precise field water and fertiliser management and agricultural decision-making. Full article

Accurate monitoring of the crop water status is of great significance for agricultural water management. To address the limitations of traditional spectral models that neglect the synergistic effects of environmental factors, this study aimed to improve the prediction ability of winter wheat water status by integrating multi-source data and machine learning algorithms. The results demonstrated significant improvements in prediction accuracy when environmental factors were integrated with hyperspectral data. During the jointing, heading, and filling stages, the prediction accuracy of the winter wheat plant water content model based on canopy hyperspectral fusion environmental factors (temperature and soil water content) was significantly higher than that based on the canopy spectral data model. The model performance (R²) increased from 0.74, 0.59, and 0.70 to 0.82, 0.69, and 0.76, respectively. The SVM-based full-growth-stage fusion model exhibited superior performance (R² = 0.85, RMSE = 5.10%, RE = 7.79%), achieving accuracy improvements of 3.53%, 23.19%, and 11.84% compared to three key growth-period models. This study confirms that integrating canopy hyperspectral data with environmental factors systematically enhances the generalization capability and accuracy of winter wheat water content prediction, providing a reliable technical solution for precision irrigation and innovative agricultural development in the future. Full article

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R² = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m²) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development. Full article

Water scarcity and spatial variability in soil fertility are key constraints to stable grain production in the Huang-Huai-Hai Plain. However, the interaction mechanisms between regulated deficit irrigation and soil fertility influencing yield formation and water-nitrogen use efficiency in winter wheat remain unclear. In this study, a two-year field experiment (2022–2024) was conducted to investigate the effects of two irrigation regimes—regulated deficit irrigation during the heading to grain filling stage (D) and full irrigation (W)—under four soil fertility levels: F1 (N: P: K = 201.84: 97.65: 199.05 kg ha⁻¹), F2 (278.52: 135: 275.4 kg ha⁻¹), F3 (348.15: 168.75: 344.25 kg ha⁻¹), and CK (no fertilization). The results show that aboveground dry matter accumulation, total nitrogen content, pre-anthesis dry matter and nitrogen translocation, and post-anthesis accumulation significantly increased with fertility level (p < 0.05). Regulated deficit irrigation promoted the contribution of post-anthesis dry matter to grain yield under the CK and F1 treatments, but suppressed it under the F2 and F3 treatments. However, it consistently enhanced the contribution of post-anthesis nitrogen to grain yield (p < 0.05) across all fertility levels. Higher fertility levels prolonged the grain filling duration by 18.04% but reduced the mean grain filling rate by 15.05%, whereas regulated deficit irrigation shortened the grain filling duration by 3.28% and increased the mean grain filling rate by 12.83% (p < 0.05). Grain yield significantly increased with improved fertility level (p < 0.05), reaching a maximum of 9361.98 kg·ha⁻¹ under the F3 treatment. Regulated deficit irrigation increased yield under the CK and F1 treatments but reduced it under the F2 and F3 treatments. Additionally, water use efficiency exhibited a parabolic response to fertility level and was significantly enhanced by regulated deficit irrigation. Nitrogen partial factor productivity (NPFP) declined with increasing fertility level (p < 0.05); Regulated deficit irrigation improved NPFP under the F1 treatment but reduced it under the F2 and F3 treatments. The highest NPFP (41.63 kg·kg⁻¹) was achieved under the DF1 treatment, which was 54.81% higher than that under the F3 treatment. TOPSIS analysis showed that regulated deficit irrigation combined with the F1 fertility level provided the optimal balance among yield, WUE, and NPFP. Therefore, implementing regulated deficit irrigation during the heading–grain filling stage under moderate fertility (F1) is recommended as the most effective strategy for achieving high yield and efficient resource utilization in winter wheat production in this region. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

To solve the issue that existing yield prediction methods do not fully capture the interaction between multiple factors, we propose a winter wheat yield prediction framework with triple cross-attention for multi-source data fusion. This framework consists of three modules: a multi-source data processing module, a multi-source feature fusion module, and a yield prediction module. The multi-source data processing module collects satellite, climate, and soil data based on the winter wheat planting range, and constructs a multi-source feature sequence set by combining statistical data. The multi-source feature fusion module first extracts deeper-level feature information based on the characteristics of different data, and then performs multi-source feature fusion through a triple cross-attention fusion mechanism. The encoder part in the production prediction module adds a graph attention mechanism, forming a dual branch with the original multi-head self-attention mechanism to ensure the capture of global dependencies while enhancing the preservation of local feature information. The decoder section generates the final predicted output. The results show that: (1) Using 2021 and 2022 as test sets, the mean absolute error of our method is 385.99 kg/hm², and the root mean squared error is 501.94 kg/hm², which is lower than other methods. (2) It can be concluded that the jointing-heading stage (March to April) is the most crucial period affecting winter wheat production. (3) It is evident that our model has the ability to predict the final winter wheat yield nearly a month in advance. Full article

The aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base as the subject. UAV-mounted multispectral sensors collected images at jointing, heading, pre-grouting, and late grouting stages. Canopy spectral reflectance was extracted using image segmentation, and vegetation indices were calculated. Correlation analysis identified highly relevant indices with LNC. Support Vector Regression (SVR), Random Forest (RF), Ridge Regression (RR), K-Nearest Neighbors (K-NN), and ensemble learning algorithms (Voting and Stacking) were employed to model the relationship between selected vegetation indices and LNC. Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Results showed that the Voting-based ensemble learning model outperformed other models. At the pre-grouting stage, this model achieved an R² of 0.85 and an RMSE of 1.57 for the training set, and an R² of 0.82 and an RMSE of 1.64 for the testing set. This study provides a theoretical basis and technical reference for monitoring LNC in winter wheat at key growth stages using low-altitude multispectral sensors, supporting precision agriculture and variety evaluation. Full article

Estimating the actual evapotranspiration (ET_{c act}) of cropland in arid areas, exploring the time trend, and analyzing periodic variation are the key to long-term assessment of water resource availability and regional drought. The Penman formula has a strong ability to characterize reference crop evapotranspiration (ET_o). However, the application of this formula may be limited in the absence of a complete set of climate data. While previous studies have investigated K_{c act} in China, few have employed localized K_c values to systematically analyze long-term periodic fluctuations in ET_{c act} under climate variability conditions. Therefore, this study aimed to evaluate the applicability of nine ET_o estimation models in the Loess Plateau of China, calculate actual crop coefficients (K_{c act}) for spring maize and winter wheat, and examine the temporal trend and periodicity of ET_{c act} for long-term (1961–2018) continuous cropping of spring maize and winter wheat in the study area. The Mann–Kendall test and continuous wavelet transform (CWT) were used to obtain the temporal trend and periodicity of ET_{c act}. The results were as follows: (1) Priestley–Taylor (Prs–Tylr), based on radiation, and the 1985 Hargreaves–Samani (Harg), based on temperature, can be used when meteorological data are limited. It should be noted that among the models evaluated in this study, except for FAO56-PM, only the Harg equation is compatible with K_c-ET_o due to established conversion factors. (2) The K_{c act} of spring maize at the seeding–jointing stage and the earning–filling stage was 12% and 10% lower than the value recommended by FAO, respectively. For K_{c act} of winter wheat, it was 65% higher, 31% lower, and 85% higher than the FAO experience values in the rejuvenation–jointing stage, heading–grouting stage, and grouting–harvest stage. (3) Winter wheat, through its ET_{c act} cycle synchronized with precipitation and excellent water balance, can effectively alleviate regional drought. It is recommended to be included in the promotion of drought resistance policies. Full article

Fusarium head blight is one of the greatest threats to global wheat production. Despite the special attention paid by researchers to resistance genetics, the stability of resistance and the expression of its epidemiological relationships have not been tested in depth. As most studies only present data on visual symptoms, in this study, we present data from four experiments. Here, 15–40 genotypes were tested with four and eight isolates (inocula) in 3–4-year experiments, with 32, 24, 36, and 12 epidemic situations used to determine the disease index (DI), Fusarium-damaged kernels (FDKs), and DON. All genotypes were tested for stability by the variance across epidemics, and the b value of the linear function was considered. Both indices were suitable for measuring stability/instability, but the variance results were more closely correlated with the experimental data than the b value, known as the stability index (SI). The use of variance is recommended due to its simplicity and reliability. In the first test, the rate of maximum/minimum variance for DI, FDK, and DON differed 15-, 20-, and 120-fold, respectively. In the second test, the same rates were 200, 400, and over 4000, with the other tests exhibiting similar tendencies. The traits differ, the epidemics vary, and a dependence on resistance level can be proven. The genotype ranking varies strongly in different epidemics, with approximately 50% of the correlations between variety responses being insignificant. Therefore, many epidemics are needed to obtain a reliable picture of the adaptation ability of the resistance traits and their stability. Approximately 25% of the genotypes tested belong to the most stable group. About 35% were discarded, and in the 40% medium, we observed both highly unstable and moderately stable genotypes. Principal component analysis (PCA) of the three traits in the experiments showed a confirmatory, nearly uniform distribution of genotypes, with a different footprint or “identity card” present for each genotype. The genotypes for the traits belong to one or two groups, although sometimes individual genotypes seem to be independent. No strict rule was found. This underlines the necessity of considering the plant’s traits (Di, FDK, and DON) in resistance testing. Highly resistant winter wheat lines could also be bred with very low variance and SI values and very high stability (SI values lower than 0.3). Of the traits, DON is the most important. With this methodology, variety registration also becomes possible. The epidemiological aspect has a decisive role in resistance studies, and without identifying stability in FHB resistance, no food safety estimates can be made. Full article

The allelic diversity within genes controlling the vernalization requirement (VRN1) and photoperiod response (PPD1) determines the ability of wheat to adapt to a wide range of environmental conditions and influences grain yield. In this study, allelic variations at the VRN-A1, VRN-B1, VRN-D1 and PPD-D1 genes were studied for 89 accessions of Triticum compactum from different eco-geographical regions of the world. The collection was evaluated for heading date in both field and greenhouse experiments under a long photoperiod and without vernalization. Based on heading date characteristics, 52 (58.4%) of the genotypes had a spring growth habit, and all of them carried at least one dominant VRN1 allele, while 37 (41.6%) accessions had a winter growth habit and carried the triple recessive allele combination. The photoperiod-sensitive Ppd-D1b allele was detected in 85 (95.5%) accessions and the insensitive Ppd-D1a allele in four (4.5%) accessions. A total of 10 phenology gene profiles (haplotypes) were observed at four major genes in the T. compactum germplasm collection. The LSD test revealed significant differences in the mean heading date among the different spring phenology gene profiles, both in greenhouse and field conditions. In addition, 21 microsatellite markers (simple sequence repeats, SSRs) were used to assess the genetic diversity in the collection. The 21 SSR markers amplified a total of 183 alleles across all the genotypes, with a mean of 3.2 alleles per locus. The polymorphic information content ranged from 0.49 to 0.94, with a mean of 0.84. The results of this study may be useful for both T. compactum and common wheat breeding programs as a source of agronomic traits. Full article