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We selected 46 core winter–spring wheat cultivars from China's main wheat-producing areas as experimental materials to clarify the differences in the physiological parameters of their cold resistance and provide a theoretical basis and high-quality germplasm for cold resistance breeding. We investigated differences in their cold resistance under field conditions for two consecutive years, and determined the physiological parameters of the cold resistance, yield, and quality indicators of different winter–spring wheat cultivars. Our results showed that the cold resistance of winter wheat cultivars was higher than that of spring wheat cultivars. The chlorophyll (Chl), soluble sugar (SS), soluble protein (SP), and free proline (Pro) contents of different winter–spring wheat cultivars were positively correlated with cold resistance, and malondialdehyde (MDA) content was negatively correlated with cold resistance. The five physiological parameters can be used as physiological indicators for the breeding of cold-resistant cultivars. The cold resistance, yield, and quality indicators of different spring and winter wheat cultivars were comprehensively evaluated by using the average membership value and comprehensive score. It was found that the average membership value and comprehensive score of winter wheat cultivars were higher than those of spring wheat cultivars. Through classification using the K-means method, the cold-resistant, high-yield, and high-quality cultivars were screened out, namely, Jimai23 (JM23), Jimai44 (JM44), Shannong57 (SN57), and Jinmai 919 (JM919). [ABSTRACT FROM AUTHOR]

The mass of nutrients accumulated in the vegetative parts of winter wheat (WW) in the period from the beginning of booting to the full flowering stage (Critical Cereal Window, CCW) allows for the reliable prediction of the grain yield (GY) and its components, and the grain protein content (GPC) and its yield. This hypothesis was verified in a one-factor field experiment carried out in the 2013/2014, 2014/2015, and 2015/2016 growing seasons. The field experiment included seven nitrogen-fertilized variants: 0, 40, 80, 120, 160, 200, and 240 kg N ha−1. The N, P, K, Ca, Mg, Fe, Mn, Zn, and Cu content in wheat vegetative parts (leaves, stems) was determined in two growth stages: (i) beginning of booting (BBCH 40) and (ii) full flowering (BBCH 65). We examined the response of eight WW traits (ear biomass at BBCH 65, EAB; grain yield, GY; grain protein content, GPC; grain protein yield, GPY; canopy ear density, CED; number of grains per ear, GE; number of grains per m−2—canopy grain density, CGD; and thousand grain weight, TGW) to the amount of a given nutrient accumulated in the given vegetative part of WW before flowering. The average GY was very high and ranged from 7.2 t ha−1 in 2016 to 11.3 t ha−1 in 2015. The mass of ears in the full flowering stage was highest in 2016, a year with the lowest GY. The highest N mass in leaves was also recorded in 2016. Only the biomass of the stems at the BBCH 65 stage was the highest in 2015, the year with the highest yield. Despite this variability, 99% of GY variability was explained by the interaction of CGD and TGW. Based on the analyses performed, it can be concluded that in the case of large yields of winter wheat, GE is a critical yield component that determines the CGD, and in consequence the GY. The leaf nutrient mass at the BBCH 40 stage was a reliable predictor of the GPC (R2 = 0.93), GPY (0.92), GE (0.84), and CED (0.76). The prediction of the GY (0.89), CGD (0.90), and TGW (0.89) was most reliable based on the leaf nutrient mass at the BBCH 65 stage. The best EAB prediction was obtained based on the mass of nutrients in WW stems at the BBCH 65 stage. The magnesium accumulated in WW parts turned out to be, with the exception of TGW, a key predictor of the examined traits. In the case of the TGW, the main predictor was Ca. The effect of Mg on the tested WW traits most often occurred in cooperation with other nutrients. Its presence in the developed stepwise regression models varied depending on the plant part and the WW trait. The most common nutrients accompanying Mg were micronutrients, while Zn, Fe, Mn, and Ca were the most common macronutrients accompanying Mg. Despite the apparently small impact of N, its yield-forming role was indirect. Excessive N accumulation in leaves in relation to its mass in stems, which appeared in the full flowering phase, positively impacted the EAB and GPC, but negatively affected the GE. Increasing the LE/ST ratio for both Mg and Ca resulted in a better formation of the yield components, which, consequently, led to a higher yield. This study clearly showed that nutritional control of WW during the CCW should focus on nutrients controlling N action. [ABSTRACT FROM AUTHOR]

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model's representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model's generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model's extraction accuracy. (3) The model's input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model). [ABSTRACT FROM AUTHOR]

Novel recycled fertilizers could help close environmental nutrient cycles in the circular economy. To better understand their performance and residual value, commercially available biobased nitrogen (N) and phosphorus (P) fertilizers (BBFs) were tested in a two-year crop cycle of winter wheat and ryegrass. The N fertilizer replacement value of N-BBFs ranged from 47 to 80% in the main crop. Not all BBFs led to a similarly high N concentration as the mineral reference in the wheat straw. However, full and early fertilization with incorporation could make the fertilizing effect of N-BBFs more reliable. The P fertilizer replacement value ranged between 105 and 161% for the crop cycle. We assume that the N contained in biobased phosphorus fertilizers can be seen as unproblematic for losses during winter and can serve as a starter fertilizer already present in the soil for the succeeding crop in spring. In general, biobased P fertilizers had a higher residual value than biobased N fertilizers. However, these residual values were comparable to those of mineral fertilizer references. While P-BBFs proved to be a sustainable and reliable nutrient source for a crop cycle, the N-BBFs used as the main crop fertilizer were found to be more prone to environmental influences. [ABSTRACT FROM AUTHOR]

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time and effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning high-photosynthetic-efficiency wheat varieties. The objective of this research is to develop a multi-stage predictive model encompassing nine photosynthetic indicators at the field scale for wheat breeding. These indices include soil and plant analyzer development (SPAD), leaf area index (LAI), net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture efficiency (Fv'/Fm'), and photochemical quenching coefficient (qP). The ultimate goal is to differentiate high-photosynthetic-efficiency wheat varieties through model-based predictions. This research gathered red, green, and blue spectrum (RGB) and multispectral (MS) images of eleven wheat varieties at the stages of jointing, heading, flowering, and filling. Vegetation indices (VIs) and texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), and BP Neural Network (BPNN)) were employed to construct predictive models for nine photosynthetic indices across multiple growth stages. Furthermore, the research conducted principal component analysis (PCA) and membership function analysis on the predicted values of the optimal models for each indicator, established a comprehensive evaluation index for high photosynthetic efficiency, and employed cluster analysis to screen the test materials. The cluster analysis categorized the eleven varieties into three groups, with SH06144 and Yannong 188 demonstrating higher photosynthetic efficiency. The moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, and Guigu 820, totaling seven varieties. Xinmai 916 and Jinong 114 fall into the category of lower photosynthetic efficiency, aligning closely with the results of the clustering analysis based on actual measurements. The findings suggest that employing UAV-based multi-source remote sensing technology to identify wheat varieties with high photosynthetic efficiency is feasible. The study results provide a theoretical basis for winter wheat phenotypic monitoring at the breeding field scale using UAV-based multi-source remote sensing, offering valuable insights for the advancement of smart breeding practices for high-photosynthetic-efficiency wheat varieties. [ABSTRACT FROM AUTHOR]

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations. [ABSTRACT FROM AUTHOR]

Mapping and monitoring crops are the most complex and difficult tasks for experts processing and analyzing remote sensing (RS) images. Classifying crops using RS images is the most expensive task, and it requires intensive labor, especially in the sample collection phase. Fieldwork requires periodic visits to collect data about the crop's physiochemical characteristics and separating them using the known conventional machine learning algorithms and remote sensing images. As the problem becomes more complex because of the diversity of crop types and the increase in area size, sample collection becomes more complex and unreliable. To avoid these problems, a new segmentation model was created that does not require sample collection or high-resolution images and can successfully distinguish wheat from other crops. Moreover, UNet is a well-known Convolutional Neural Network (CNN), and the semantic method was adjusted to become more powerful, faster, and use fewer resources. The new model was named Fast-UNet and was used to improve the segmentation of wheat crops. Fast-UNet was compared to UNet and Google's newly developed semantic segmentation model, DeepLabV3+. The new model was faster than the compared models, and it had the highest average accuracy compared to UNet and DeepLabV3+, with values of 93.45, 93.05, and 92.56 respectively. Finally, new datasets of time series NDVI images and ground truth data were created. These datasets, and the newly developed model, were made available publicly on the Web. [ABSTRACT FROM AUTHOR]

Surface ozone (O3) pollution has adverse impacts on the yield of winter wheat. The North China Plain (NCP), one of the globally significant primary regions for winter wheat production, has been frequently plagued by severe O3 pollution in recent years. In this study, the effects of O3 pollution on winter wheat yield and economic impact were evaluated in the NCP during the 2015–2018 seasons using the regional atmospheric chemical transport model (WRF-Chem), O3 metrics including the phytotoxic surface O3 dose above 12 nmol m−2 s−1 (POD12), and the accumulated daytime O3 above 40 ppb (AOT40). Results showed that the modeled O3, exposure-based AOT40, and flux-based POD12 increased during the winter wheat growing season from 2015 to 2018. The annual average daytime O3, exposure-based AOT40, and flux-based POD12 were 44 ppb, 5.32 ppm h, and 1.78 mmol m−2, respectively. During 2015–2018, winter wheat relative production loss averaged 10.9% with AOT40 and 14.6% with POD12. This resulted in an average annual production loss of 12.4 million metric tons, valued at approximately USD 4.5 billion. This study enhances our understanding of the spatial sensitivity of winter wheat to O3 impacts, and suggests that controlling O3 pollution during the key growth stages of winter wheat or improving its O3 tolerance will enhance food security. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB), caused by the Fusarium graminearum species complex, causes significant losses in grain yield and quality of wheat (Triticum aestivum) by inducing floret sterility. Grains become contaminated with mycotoxins, especially deoxynivalenol (DON), making them unsuitable for consumption. To clarify the impact of wheat cultivar resistance, infection level, and climate after anthesis on the efficacy of a fungicide for the control of FHB, we treated two moderately susceptible cultivars and 11 susceptible cultivars with fungicide (48% phenamacril + tebuconazole) at anthesis over two years. FHB incidence (INC), disease severity index (DSI), Fusarium-damaged kernels, DON contamination, thousand-kernel weight, and yield were evaluated under artificially inoculated and naturally infected field trials in 2018 and 2021. The results of multi-factor variance analysis show that the control efficacy with respect to INC and DSI is affected by cultivar, fungicide, infection level, and climatic conditions including the average daily temperature, average daily relative humidity, and total rainfall from anthesis to 21 days after anthesis (p < 0.01). Notably, cultivar resistance (deviance = 13.34, 9.55, and 11.22) is more important than fungicide (deviance = 5.77, 6.66, and 6.69) to control the efficacy of INC, DSI, and DON. The results also suggest that infection level appears to be more important than cultivars and fungicide to control the efficacy of INC, and more important than fungicide to control the efficacy of DSI. Total rainfall is more important than other climatic factors. Our results reveal that fungicide is more effective in moderately susceptible cultivars ('Zhengmai 9023' and 'Xinong 979', 89.5%~98.9%) and some susceptible cultivars than in other susceptible cultivars ('Zhengmai 7698' and 'Zhoumai 27', 51.9%~67.2%). Thus, integrating cultivar resistance with fungicide application can be an effective strategy for the management of FHB and DON in winter wheat in the Huang-huai-hai Plain of China. [ABSTRACT FROM AUTHOR]

Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning models in this field, few studies have analyzed the effect of the large-scale geospatial characteristics of neighboring regions on crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. The datasets used in this study included multiple types of remote sensing, meteorological, soil, crop yield, and planting area data for Anhui, China, from 2005 to 2020. The results showed that multi-source data led to higher prediction performance than single-source data, and enabled accurate prediction of winter wheat yields three months prior to harvest. Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R2 = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). Therefore, ASTGNN enhances the accuracy of crop yield prediction by incorporating geospatial characteristics. This research has implications for improving agricultural production management, promoting the development of digital agriculture, and addressing climate change in agriculture. [ABSTRACT FROM AUTHOR]

The cultivation or 'tillage' system is one of the most important elements of agrotechnology. It affects the condition of the soil, significantly modifying its physical, chemical, and biological properties, and the condition of plants, starting from ensuring appropriate conditions for sowing and plant growth, through influencing the efficiency of photosynthesis and ultimately, the yield. It also affects air transmission and the natural environment by influencing greenhouse gas (GHG) emissions potentially. Ultimately, the cultivation system also has an impact on the farmer, providing the opportunity to reduce production costs. The described experiment was established in 1998 at the Brody Agricultural Experimental Station belonging to the University of Life Sciences in Poznań (Poland) on a soil classified as an Albic Luvisol, while the described measurements were carried out in the 2022/2023 season, i.e., 24 years after the establishment of the experiment. Two cultivation methods were compared: Conventional Tillage (CT) and No Tillage (NT). Additionally, the influence of two factors was examined: nitrogen (N) fertilization (0 N—no fertilization, and 130 N–130 kg N∙ha−1) and the growth phase of the winter wheat plants (BBCH: 32, 65 and 75). The growth phase of the plants was assessed according to the method of the Bundesanstalt, Bundessortenamt and CHemische Industrie (BBCH). We present the results of soil properties, soil respiration, wheat plants chlorophyll fluorescence, and grain yield. In our experiment, due to low rainfall, NT cultivation turned out to be beneficial, as it was a key factor influencing the soil properties, including soil organic carbon (SOC) content and soil moisture, and, consequently, creating favorable conditions for plant nutrition and efficiency of photosynthesis. We found a positive effect of NT cultivation on chlorophyll fluorescence, but this did not translate into a greater yield in NT cultivation. However, the decrease in yield due to NT compared to CT was only 5% in fertilized plots, while the average decrease in grain yield resulting from the lack of fertilization was 46%. We demonstrated the influence of soil moisture as well as the growth phase and fertilization on carbon dioxide (CO2) emissions from the soil. We can clearly confirm that the tillage system affected all the parameters discussed in the work. [ABSTRACT FROM AUTHOR]

The application of organic manure is an effective way to develop sustainable agriculture. However, the application of organic manure may be associated with a potential risk of heavy metal pollution for soil and crops. In this study, the effects of organic cow manure (T1) (as base fertilizer), organic pig manure (T2) (as base fertilizer) and chemical fertilizer (T3) on winter wheat grain yields, grain quality, heavy metal concentrations and heavy metal bioconcentration factors (BCFs) in a soil–wheat system were studied from November 2021 to June 2023. The results showed that the winter wheat grain yields in the T1 and T2 treatments were lower than those in the T3 treatment by 2.57–38.0% and 10.5–25%, respectively. There were no significant differences in quality indexes of winter wheat grain among different fertilizer treatments. The concentrations of heavy metals in topsoil and winter wheat were 0.12–76.11 μg/g and 0.01–43.25 μg/g, respectively. The BCFs of heavy mental in the soil–wheat grain system was 0–2.92. In general, there were no significant differences in heavy metals' concentrations in topsoil and wheat grain among different fertilizer treatments. In summary, compared with chemical fertilizer, the short-term application of organic manures had no significant effect on heavy metals concentrations in topsoil and wheat. [ABSTRACT FROM AUTHOR]

Wheat grain productivity is different from year to year because growing environments are highly seasonally variable as a result of climate change. This study analyzed the variation in the weather conditions in the 2010–2023 growing seasons and evaluated the crop developmental phase, yield-related components, and the correlations of the variables in the southern plain of South Korea, measuring agronomic traits, including the above-ground dry weight, young-panicle length, spike number per m2, number of grains per spike, thousand-grain weight, and grain yield. The number of days in the heading and ripening phase showed less differences than the other growth phases. The thousand-grain weight showed low variations over the fourteen years observed, unlike the number of grains per spike, the marketable grain yield, and the straw yield, with comparatively high variations. The grain yield was negatively correlated with the average air temperature during the winter dormancy phase (R = −0.687, p = 0.007) and precipitation (R = −0.726, p = 0.003), but showed positive associations with the number of days in the winter dormancy phase (R = 0.597, p = 0.024) and the number of grains per spike (R = 0.809, p = 0.000). In conclusion, longer winter dormancy and a longer tillering phase delay young-panicle development but increase the number of spikes and the number of grains per spike, resulting in a higher wheat grain yield in Southern Korean weather conditions. [ABSTRACT FROM AUTHOR]

More than one-third of the global population relies on wheat as a staple food. To ultimately reduce inorganic nitrogen (N) usage through applying an organic fertilizer under drip irrigation and evaluate its effect on the yield, quality, and N utilization efficiency of winter wheat (variety Jimai 22) under various irrigation systems, an experiment was established and conducted in Yanghe Town, Jiaozhou City, from October 2020 to June 2022. The trial was designed with seven treatments, including a control (CK), to achieve a 25% total nitrogen reduction in all treatments except for CK. These treatments included drip irrigation with urea as CK, one-time application of urea through drip irrigation (FU1), one-time application of organic water-soluble fertilizer through furrow irrigation (FO1), one-time application of organic water-soluble fertilizer through drip irrigation (DO1), two-time application of organic water-soluble fertilizer through drip irrigation (DO2), one-time application of urea through drip irrigation (DU1), and two-time application of urea through drip irrigation (DU2). The results indicated that the application of a reduced N fertilizer plus an organic fertilizer significantly improved the dry matter accumulation (DMA) and the efficiency of N absorption and thus increased the grain yield. The DO2 treatment significantly exhibited a 15.5% and 16.9% increase in the DMA and the grain DMA in post-anthesis, respectively, compared to those of CK in the season of 2020–2021. Overall, the apparent nitrogen use efficiency with the drip irrigation topdressing treatments (DO1, DO2, DU1, DU2) increased significantly over two years in comparison with the urea fertilization through traditional furrow irrigation (CK), while the DO2 and DU2 treatments improved most significantly in the N use efficiency and N agronomic efficiency. Therefore, a reduced use of the inorganic N fertilizer with some organic fertilizers significantly increased the weight of thousand-grains and the yield of winter wheat, especially in the DO2 treatment, with an 11.7 t/ha and 10.9 t/ha increase, respectively, in both growing seasons of two years, while the DO2 treatment also improved the extensibility of wheat flour dough from grains harvested in both rainy (2020–2021) and less rainy (2021–2022) growing seasons. Therefore, we strongly recommend that two-time application of an organic water-soluble fertilizer through drip irrigation be the option to reduce the use of inorganic N fertilizers and increase the yield and quality of winter wheat under the conditions of this experiment. [ABSTRACT FROM AUTHOR]

The dry–hot–windy climate frequently occurs during the grain-filling stage of winter wheat on the North China Plain (NCP) and thus negatively influences wheat yield. Sprinkler irrigation can improve field temperature and humidity and can be used to mitigate dry–hot–windy climate disasters. A two-season field experiment was carried out on the NCP to test how sprinkler irrigation influences the microclimate, canopy temperature and photosynthetic traits, as well as the grain-filling process and final grain yield, when spraying 1.5–2 mm of water on dry–hot–windy days. Field experiments revealed that, compared with the no-spraying treatment, spraying with 2 mm of water each time caused the air and canopy temperatures to decrease by 2.3–7.6 °C and 4.3–9.9 °C, respectively, during and just after spraying stopped, and the temperatures returned to their previous levels approximately one hour after spraying. The air humidity increased by up to 10% during and after spraying. The photosynthesis and transpiration rates and the stomatal conductivity after spraying increased by 34–235%, 15–55% and 24–79%, respectively. The linear relationships between photosynthesis rates and transpiration rates with respect to stomatal conductivity suggest that increases in both photosynthesis and transpiration rates are the main contributors to the increase in stomatal conductivity, which is due mainly to the improved canopy temperature and humidity conditions caused by spraying practices. The grain-filling process was improved by spraying, which ultimately increased the unit grain mass by approximately 5%. One spraying event on a dry–hot–windy day influenced the field microclimate and canopy photosynthetic traits for 90 min (30 min in spraying time + 60 min after spraying). When the intensity of the dry–hot–windy climate is strong, two spraying events can be applied. Spraying 2–2.5 mm of water each time was sufficient when the leaf area index was 4–5 during the grain-filling stage of winter wheat. Spray events can have a slight effect on grain yield when a dry–hot–windy climate occurs within the last five days before harvest. [ABSTRACT FROM AUTHOR]

Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting sustainable agricultural practices. This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical yield records from Henan Province, China. The core of the model is an ensemble voting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble voting regressor and conducted an in-depth comparison of its yield prediction performance with that of other mainstream machine learning models, assessing the impact of key hyperparameters on model accuracy. This study also explored the potential of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor performed exceptionally well throughout the entire growth period, with an R2 of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the heading stage, the model's prediction performance was particularly impressive, with an R2 of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing models developed for other growth stages. Additionally, by establishing a yield spatialization model, this study mapped county-level yield predictions to the pixel level, visually illustrating the spatial differences in land productivity. These findings provide reliable technical support for winter wheat yield prediction and valuable references for crop yield estimation in precision agriculture. [ABSTRACT FROM AUTHOR]

Protection of high-yielding winter wheat (WW) with fungicides increases the productivity of nitrogen (N) present in the soil–crop system during the growing season. As a consequence of the action of fungicides, the nitrogen gap (NG) reduces. This hypothesis was verified on the basis of data from a field experiment conducted with WW during three growing seasons (2013/2014; 2014/2015, 2015/2016) in Poland. The field experiment included two crop protection systems (CP): (i) CP-0—without fungicides and CP-F—with fungicides and (ii) six N doses increased gradually by 40 kg N ha−1 from 0 to 240 kg N ha−1. The grain yield (GY) of WW treated with fungicides was significantly higher than that of the unprotected. The difference in yields between both CP systems was 17.3% on a plot fertilized with 200 kg N ha−1 (9.13 vs. 11.2 t ha−1). The fungicide yield gap increased progressively with Nf doses from 0.76 t ha−1 in the Nf control plot to 2.17 t ha−1 in the fertilized with 200 kg ha−1. The use of fungicides increased the amount of N in grain (Ngr) from 15 kg N ha−1 in the control N plot to 51 kg N ha−1 in the plot with 200 kg N ha−1. The main source of additional N in grain (Ngr) was inorganic N released from the soil (Ng89) during the WW growing season. The maximum Ng89 values were 64.4 and 83.0 kg N ha−1. These values corresponded to Nf doses of 94.4 and 80.8 kg N ha−1. The Ng89 of 70.1 kg N ha−1 conditioned 100-percentage Nf recovery. As a consequence, the prediction reliability of GY and Ngr was highest when Ng89 was used as a predictor. The net increase in the absolute NG size in response to increasing N input was significantly slower and therefore smaller in fungicide-protected than in unprotected WW. It can be concluded that the use of fungicides due to the increase in inorganic N productivity in the soil–crop system reduces the potential threat of N dispersion into the environment. In the light of the results obtained, it should be concluded that the fungicidal protection of crop plants should be treated as a factor significantly reducing the nitrogen gap and, thus, the yield gap. [ABSTRACT FROM AUTHOR]

Biogas digestate is increasingly used in organic farming to improve soil nutrient supply and sustainably increase yields. However, biogas digestate can also lead to environmentally relevant N2O emissions. The benefits, opportunities, and risks associated with the use of digestate as a fertilizer in organic farming are a subject of ongoing debate, in part due to a lack of conclusive experimental results. A field trial conducted in southern Germany examined the short-term and long-term impacts of digestate fertilization on winter wheat yield, nitrogen use efficiency, and N2O-N emissions. The four-year results from the years 2019 to 2022 are presented. Digestate was applied with a nitrogen input of up to 265 kg ha−1, with 129 kg ha−1 NH4+-N. The application of digestate resulted in a significant increase in wheat yield, with an average increase of 53% (2019) to 83% (2022) compared to the unfertilized control. It is notable that the treatment applied for the first time did not reach the yield of the long-term fertilized treatment, with a yield gap of 0.5 to 1.2 Mg ha−1 (6% to 15%). The highest N2O-N emissions (up to 3.30 kg ha−1) in the vegetation period from spring to autumn were measured in the long-term fertilized treatment. However, very high N2O-N emissions (up to 3.72 kg ha−1) also occurred in two years in winter in the unfertilized treatment. An increase in soil inorganic N stocks and N2O-N emissions was observed following the wheat harvest and subsequent tillage in all treatments. No significant differences were identified between the fertilizer treatments with regard to product-related emissions. The experimental results demonstrate that N2O-N emissions are not solely a consequence of N fertilization, but can also be attributed to tillage, post-harvest practices, and previous crops, with considerable variability depending on weather conditions. The experimental data provide comprehensive insight into the influence of cultivation, soil characteristics, and meteorological conditions on N2O-N emissions at an agricultural site in southern Germany. [ABSTRACT FROM AUTHOR]

Timely and accurate predictions of winter wheat yields are key to ensuring food security. In this research, winter wheat yield prediction models for six provinces were established using a random forest (RF) model. Two methods were employed to analyze feature variables. RF partial dependence plots were generated to demonstrate the nonlinear relationships between the feature variables and yield, and bivariate Moran's I was considered to identify the spatial associations between variables. Results showed that when environmental data from key growth periods were used for prediction model establishment, the root mean square error (RMSE) varied between 200 and 700 kg/ha, and the coefficient of determination (R2) exceeded 0.5. Feature variable analysis results indicated that the longitude, latitude, topography and normalized difference vegetation index (NDVI) were important variables. Below the threshold, the yield gradually increased with increasing NDVI. Bivariate Moran's I results showed that there was zonal distribution of meteorological elements. Within a large spatial range, the change in environmental variables due to the latitude and longitude should be accounted for in modeling, but the influence of collinearity between the feature variables should be eliminated via variable importance analysis. [ABSTRACT FROM AUTHOR]

Granule size distribution of wheat starch is an important characteristic that could affect the functionality of wheat (Triticum aestivum L.) products. Lodging is a major limiting factor for wheat production. Few studies have been conducted to clarify how lodging influences the granule size distribution and viscosity parameters of starch in wheat grains. Two growing seasons, two high-yield winter wheat cultivars, and five artificial lodging treatments were imposed. The results indicated that lodging significantly reduced the content of starch and increased that of protein. Additionally, lodging caused a marked drop in both starch and protein yields. The relative loss of grain yield, starch yield, harvest index, and protein yield all differed remarkably among lodging treatments with a ranking of L2 > L1 > L4 > L3. Lodging also led to a reduction in the proportion (both by volume and by surface area) of B-type granules and a corresponding increase in that of A-type granules, and the more serious the lodging degree, the greater effect on the changes in these proportions. The smaller starch granules predominated in number, even though their collective contribution to the overall volume is was relatively minor. Meanwhile, it was found that the peak viscosity, hold viscosity, final viscosity, breakdown viscosity, and rebound value of wheat starch were significantly decreased by lodging. Correlation analysis showed that the peak and final viscosities were negatively correlated with volume percentages of A-type starch granules, but were positively correlated with B-type granules. This indicates that B-type granules have higher peak and final viscosities compared with A-type granules in wheat kernels. Lodging can reduce the proportion of B-type starch granules, and thus reduce the peak and the final viscosity in wheat grain. [ABSTRACT FROM AUTHOR]

This study investigates the impact of corn straw return and nitrogen application rates on ammonia volatilization and yield enhancement under field conditions, in order to reduce emissions while increasing crop yield. During the winter wheat season, a fissure area design was implemented, comprising three levels of straw return in the main area and three distinct nitrogen fertilizer levels in the subsidiary area, for a total of nine treatments. The results can be summarized as follows: (1) The ammonia emissions flux initially increased followed by a decrease, and was primarily concentrated within the first 14 days after fertilization, with a peak observed at 4–5 days before decreasing. Notably, nitrogen fertilizer significantly affected the cumulative ammonia emissions, ranging from 0.019 to 1.786 kg·hm−2·d−1 and 0.013 to 1.693 kg·hm−2·d−1 across the two seasons. (2) The soil with a higher nitrogen application rate exhibited elevated levels of inorganic nitrogen content and urease activity under the same straw return level. Maintaining a consistent nitrogen application level, the return of straw to the field increased the cumulative ammonia discharge, inorganic nitrogen content, and urease activity. (3) The interaction between straw return and nitrogen fertilizer substantially affected crop yield. Specifically, during the winter wheat season, the optimal combination for reducing ammonia emissions and enhancing yield was observed under straw return (both half or full) combined with 180 kg·hm−2 nitrogen application. Notably, the reduction of soil emissions and winter wheat yield augmentation were feasible through appropriate corn straw return in the preceding season. [ABSTRACT FROM AUTHOR]

Underground coal mining in coal-grain overlapped areas leads to land subsidence and deformation above the goaf, damaging cultivated land. Understanding the influencing process of coal mining on cultivated land and crops is important for carrying out timely land reclamation and stabilizing crop yield. Research has been carried out by using crop growth parameters to evaluate the damaging degree of cultivated land when the mining subsidence is stable, but few studies focus on the influence of land damage on crop growth when the subsidence is unstable during coal mining. Therefore, this study tracked the three growth stages of winter wheat by using UAV multispectral imagery to explore the dynamic influence of underground mining on winter wheat aboveground biomass (AGB). Firstly, a winter-wheat-AGB estimation model (R2 = 0.89, RMSE = 2.18 t/ha) was developed by using vegetation indexes (VIs), textures, and terrain data extracted from UAV imagery. Secondly, based on the winter-wheat-AGB estimation model, the winter wheat AGB was successfully estimated and mapped at different growth stages. The AGB of winter wheat in the coal mining-affected area was approximately 5.59 t/ha at the reviving stage, 8.2 t/ha at the jointing stage, and 15.6 t/ha at the flowering stage. Finally, combined with the progress of coal mining, the dynamic changing process of crops during underground mining can be inferred by analyzing the spatiotemporal variation in winter wheat AGB. Results showed that, in the dip direction, winter wheat AGB at the flowering stage was the highest at the compression zone, followed by the inner stretch zone, outer stretch zone, and neutral zone. The distance from the waterlogged area and the existence of cracks were found to be the important moderating variables affecting the crop growth status in the mining subsidence area. In the strike direction, there were significant differences in the wheat AGB-affected area as the mining proceeded. Even areas where AGB had previously significantly increased gradually transitioned to significant decreases with the end of mining. The research explores the dynamic changes in winter wheat AGB and land damage status during coal mining. It provides a rapid and non-destructive land-damage-monitoring method to protect cultivated land in mining areas. [ABSTRACT FROM AUTHOR]

The global phosphate (P) rock shortage has become a significant challenge. Furthermore, the misalignment between crops, soil, and P usage exacerbates P rock wastage in agriculture. The distinctions among various types of phosphorus fertilizers influence the phosphorus cycle, which subsequently impacts biomass, the number of grains per ear, the weight of a thousand grains, and, ultimately, the overall yield. In a four-year field experiment conducted from 2017 to 2021, we assessed the impact of various P fertilizer types on crop yield in a continuous wheat–maize rotation system. Prior to planting the crops, P fertilizers were applied as base fertilizers at a rate of 115 kg P2O5 ha−1 during the wheat season and 90 kg P2O5 ha−1 during the maize season. Additionally, nitrogen (N) was applied at rates of 120 kg ha−1 for wheat and 180 kg ha−1 for maize. The P fertilizers used included ammonium dihydrogen phosphate, ammonium polyphosphate, calcium–magnesia phosphate, ammonium phosphate, and calcium superphosphate. Urea was used as the N fertilizer with a split application—60% at planting and 40% at the jointing stage for wheat or the V12 (twelve leaf collar) stage for maize. The results showed that different P fertilizers increased the average yield of wheat and maize by 21.2–38.0% and 9.9–16.3%, respectively. It was found that ammonium polyphosphate, calcium superphosphate, and monoammonium phosphate were more suitable for application in a summer maize–winter wheat rotation system on loess soil. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing technology plays a vital role in advancing modern precision agriculture due to its non-destructive and efficient nature. To achieve accurate monitoring of winter wheat chlorophyll content, this study utilized 68 sets of chlorophyll content data and hyperspectral measurements collected during the jointing stage of winter wheat over two consecutive years (2019–2020), under various fertilization types and nitrogen application levels. Continuous wavelet transform was applied to transform the original reflectance, ranging from 21 to 210, and the correlation matrix method was utilized to identify the spectral index at each scale, with the highest correlation to winter wheat chlorophyll content as the optimal spectral index combination input. Subsequently, winter wheat chlorophyll content prediction models were developed using three machine learning methods: random forest (RF), support vector machine (SVM), and a genetic algorithm-optimized backpropagation neural network (GA-BP). The results indicate that the spectral data processed through continuous wavelet transform at seven scales, from 21 to 27, show the highest correlation with winter wheat chlorophyll content at a scale of 26, with a correlation coefficient of 0.738, compared with the correlation of 0.611 of the original reflectance, and the accuracy is improved by 20.7%. The average highest correlation value between the spectral index at scale 26 and winter wheat chlorophyll content is 0.752. As the scale of wavelet transform increases, the correlation between the spectral index and winter wheat chlorophyll content and the accuracy of the predictive model show a trend of first increasing and then decreasing. The optimal input variables for predicting winter wheat chlorophyll content and the best machine learning method are the spectral data at a scale of 26 processing combined with the GA-BP model. The optimal predictive model has a validation set coefficient of determination (R2) of 0.859, root mean square error (RMSE) of 1.366, and mean relative error (MRE) of 2.920%. The results show that the prediction model can provide a technical basis for improving the hyperspectral inversion accuracy of winter wheat chlorophyll and modern precision agriculture. [ABSTRACT FROM AUTHOR]

This study was carried out to evaluate the effects of the combined application of water-retaining agents and nitrogen fertilizers on soil physicochemical properties, bacterial communities, and root growth under winter wheat planting mode in the Guanzhong area of Shaanxi Province. Based on the positioning experiment of dry farming in the loessal soil area of Shaanxi Province, four treatments were set up by using the tillage method of subsoiling + rotary tillage and straw returning: only fertilization (U), only water retention agent sodium polyacrylate (C3H3NaO2)n (S), combined use of water-retaining agent sodium polyacrylate (C3H3NaO2)n and fertilizer (US), and control group CK (no treatment). The ultra-high-throughput sequencing of 16S rRNA genes of soil bacteria was performed by the Illumina Hiseq platform, and the effects of different tillage measures on soil bacterial diversity and community structure were analyzed. In addition, the effects of these tillage measures on soil physicochemical properties and winter wheat root length density at booting and flowering stages were evaluated. The results indicated that the combination of the water-retaining agent and fertilizer markedly enhanced the contents of ammonium nitrogen, available phosphorus, and available potassium in the 0~20 cm soil layer, significantly increased the soil moisture content, and promoted the deep growth of roots. The root length density was 4.70 times higher than that of the control group at the booting stage. In addition, the combined application alleviated the decrease in soil microbial diversity caused by individual fertilization, especially significantly increasing the abundance of Gemmatimonadetes, Acidobacteria, and Planctomycetes in the 0~10 cm soil layer. This study reveals the potential of the combined use of water retention agents and fertilizers to optimize the soil environment and enhance winter wheat yield, which provides a scientific basis for improving local agricultural practices. [ABSTRACT FROM AUTHOR]

With global climate change, flooding events are becoming more frequent. However, the mechanism of how waterlogging stress affects crop roots needs to be studied in depth. Waterlogging stress can also lead to soil nitrogen and phosphorus loss, resulting in agricultural surface pollution. The aim of this study is to clarify the relationship between soil nitrogen and phosphorus distribution, root growth characteristics, and nitrogen and phosphorus loss in runoff water under waterlogging stress during the winter wheat seedling stage. In this paper, Zhengmai 136 was selected as the experimental material, and two water management methods (waterlogging treatment and non-waterlogging control treatment) were set up. Field experiments were conducted at the Wudaogou Hydrological Experimental Station in 2022 to assess the nitrogen and phosphorus concentrations in runoff water under waterlogging stress. The study also aimed to analyze the nitrogen and phosphorus content and the root distribution characteristics in different soil layers under waterlogging stress. The results showed as the following: 1. Waterlogging stress increased the characteristic parameters of winter wheat roots in both horizontal and vertical directions. Compared with the control treatment, the root length increased by 1.2–29.9% in the waterlogging treatment, while the root surface area and volume increased by an average of 3.1% and 41.9%, respectively. 2. Nitrogen and phosphorus contents in waterlogged soils were enriched in the 0–20 cm soil layer, but both tended to decrease in the 20–60 cm soil layer. Additionally, there was an inverse relationship between the distribution of soil nutrients and the distribution of wheat roots. 3. During the seedling stage of winter wheat, nitrogen loss was the main factor in the runoff water. In addition, nitrate nitrogen concentration averaged 55.2% of the total nitrogen concentration, while soluble phosphorus concentration averaged 79.1% of the total phosphorus concentration. 4. The results of redundancy analysis demonstrated that available phosphorus in the soil was the key environmental factor affecting the water quality of runoff water. Total phosphorus and dissolved phosphorus in the water were identified as the dominant factors influencing root growth. [ABSTRACT FROM AUTHOR]

No-tillage of summer maize after the harvest of winter wheat is the primary agricultural practice on the North China Plain. However, prolonged no-tillage without deep tillage practices negatively impacts soil properties, which is detrimental to the growth and yield of summer maize. In this study, Xianyu 688 and Jifeng 2 were used as test maize materials, no-tillage and surface fertilizing with normal nitrogen (N) (BC240), no-tillage and surface fertilizing with N reduction (BC180), subsoiling layered fertilization with normal N (FC240) and subsoiling layered fertilization with N reduction (FC180)were designed, in order to assess root distribution, N utilization and grain yield of summer maize. In the two maize cultivars, the FC240 and FC180 treatments significantly reduced soil bulk density in the 10–50 cm depth soil layer compared to the other two treatments, and also increased total N content in the 20–50 cm depth soil layer. Compare BC 240 treatment, the FC240 treatment significantly increased root length in the 20–40 cm soil layer and root rap bleeding. Additionally, the FC240 and FC180 treatments enhanced dry matter and N accumulation, grain yield, N uptake efficiency and N fertilizer partial factor productivity. In various treatment, Xianyu 688 exhibited increased grain yield, N uptake efficiency and N fertilizer partial factor productivity compare with Jifeng 2. When employing a total N application level of 180–240 kg N/hm2, the synergistic improvement of summer maize grain yield and N efficiency can be achieved by incorporating subsoiling combined with layered nitrogen application. [ABSTRACT FROM AUTHOR]

This document, titled "Climate Change and Agriculture—Sustainable Plant Production," discusses the impact of climate change on plant growth and agricultural production. It highlights how climate change can affect the growing season, growth rate, and distribution of plants. While climate change can extend the growing season and promote crop photosynthesis, it can also lead to imbalanced nutrient absorption, hinder photosynthesis, and affect crop yields. Irregular precipitation patterns and extreme weather events can cause hypoxia and nutrient loss in plant roots, damage plants directly, and increase the range of diseases and pests. Climate change also affects soil moisture, temperature, microbial activity, nutrient cycling, and quality, which in turn affects plant growth. The document emphasizes the importance of understanding the mechanisms through which climate change impacts plants and agriculture and taking adaptive measures to promote sustainable agricultural development. The document also introduces a special issue of the journal Agronomy, which focuses on the interaction between climate change and agriculture and includes research on crops, soils, and adaptation strategies. The research covers topics such as the impact of temperature/climate change on rice, wheat, and olive oil; the adaptation strategies of crops; the impact of climate change on soil nutrient cycling; and the spatial-temporal characteristics of vegetation. The document provides summaries of some of the articles included in the special issue, which discuss topics such as the stoichiometric relationship of soil nutrients, the impact of climate warming and aridification on vegetation, the impact of heat stress on rice growth, the physiological characteristics [Extracted from the article]

TFC (10% thifluzamide–fludioxonil–clothianidin) is a novel wheat seed-coating agent. In the field, we confirmed that 10% TFC plays a positive role in preventing soil-borne diseases and promoting wheat seedling growth. However, its effects on rhizosphere microecology and the underlying molecular mechanism are not fully understood. Field trials revealed a positive effect on the biomass, plant height, and root length of wheat sharp eyespots in a Yingshang field, with 95.3% control efficiency. The effects of 10% TFC on the rhizosphere soil microbiome of young wheat plants were evaluated using high throughput sequencing technology. The results demonstrated that seed-coating agents significantly changed bacterial and fungal communities, and reduced the number of bacteria but increased the number of fungi. Sequence analysis revealed that the abundance of Proteobacteria, Actinobacteria, and Patescibacteria in bacteria and Ascomycota, Mortierellomycota, and Basidiomycota in fungi were significantly enriched, which have been reported as being beneficial for plant growth and pathogen resistance. In contrast, the abundance of Mucoromycota in fungi was reduced, and most of the related genera identified were pathogenic to plants. In this study, 15-day-old wheat plant tissues treated with 10% TFC were subjected to global transcriptome analysis by RNA sequencing to provide insights into the effects of 10% TFC on seedling growth. The comparative analysis of Triticum aestivum L. libraries identified 8286 differentially expressed genes (DEGs), of which 2290 and 5996 genes were up- and downregulated in seedling growth in the presence of 10% TFC, respectively. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were performed for up- and downregulated DEGs separately, showing that these DEGs were enriched for terms related to the phenylpropanoid biosynthesis pathway, the protein products of which promote cell differentiation and seedling growth. This research provides comprehensive insights into its effects on wheat seedling growth and the rhizosphere microecology of seed coatings and provides important insights into their regulation and into understanding the potential benefits of seed coatings in disease management and plant growth promotion. [ABSTRACT FROM AUTHOR]

Incorporating crop residues into the soil is an effective method for improving soil carbon sequestration, fertility, and crop productivity. Such potential benefits, however, may be offset if residue addition leads to a substantial increase in soil greenhouse gas (GHG) emissions. This study aimed to quantify the effect of different crop residues with varying C/N ratios and different nitrogen (N) fertilizers on GHG emissions, yield, and yield-scaled emissions (GHGI) in winter wheat. The field experiment was conducted during the 2018–2019 winter wheat season, comprising of four residue treatments (no residue, maize residue, soybean residue, and maize-soybean mixed residue) and four fertilizer treatments (control, urea, manure, and manure + urea). The experiment followed a randomized split-plot design, with N treatments as the main plot factor and crop residue treatments as the sub-plot factor. Except for the control, all N treatments received 150 kg N ha−1 season−1. The results showed that soils from all treatments acted as a net source of N2O and CO2 fluxes but as a net sink of CH4 fluxes. Soybean residue significantly increased soil N2O emissions, while mixed residue had the lowest N2O emissions among the three residues. However, all residue amendments significantly increased soil CO2 emissions. Furthermore, soybean and mixed residues significantly increased grain yield by 24% and 21%, respectively, compared to no residue amendment. Both soybean and mixed residues reduced GHGI by 25% compared to maize residue. Additionally, the urea and manure + urea treatments exhibited higher N2O emissions among the N treatments, but they contributed to significantly higher grain yields and resulted in lower GHGI. Moreover, crop residue incorporation significantly altered soil N dynamics. In soybean residue-amended soil, both NH4+ and NO3− concentrations were significantly higher (p < 0.05). Conversely, soil NO3− content was notably lower in the maize-soybean mixed residue amendment. Overall, our findings contribute to a comprehensive understanding of how different residue additions from different cropping systems influence soil N dynamics and GHG emissions, offering valuable insights into effective agroecosystems management for long-term food security and soil sustainability while mitigating GHG emissions. [ABSTRACT FROM AUTHOR]

Increased nitrogen (N) application has profound effects on CO2 flux in croplands. The aim of this study is to investigate the effects of long-term N addition on soil respiration (SR) in a rainfed winter wheat (Triticum aestivum L.) field in the Loess Plateau of China. Two wheat cultivars were planted under three levels of N application (0, 180, and 360 kg N ha−1 year−1) in non-irrigated cropland from 2004 to 2013. The diurnal and seasonal SR variations and abiotic and biotic factors were measured during the growing seasons in 2012–2013. The results showed that N180 and N360 increased the cumulative CO2 flux by 30.3% and 32.4% on average after 5 and 10 years of N application, respectively. Multiple regressions revealed that the seasonal SR was mainly controlled by the soil temperature (ST), at a depth of 8 cm, and the leaf area index. Diurnal SR was mainly controlled by the ST and the net photosynthesis rate. Long-term N application stimulated SR by increasing the photosynthetic leaf area and temperature sensitivity. Overall, N application at a rate of 360 kg N ha−1 year−1 did not reach the threshold for limiting SR in the investigated semi-humid rainfed wheat cropland in the Loess Plateau of China. [ABSTRACT FROM AUTHOR]

Plastic film mulching (PM) is a useful agronomic means to adapt to the environmental conditions of dryland agriculture and improve crop production. To improve the theoretical framework of PM technology, this study focuses on the interaction between the soil temperature change caused by PM and crop growth. The definition, action mechanism, and simulation of the compensatory effect of PM on growing degree days are introduced to reveal the effect of soil temperature under PM on crop development and growth. Our summary shows that the strength of the warming effect changes with the growth and development of crops, strengthening during the early stage of crop growth and gradually weakening as a crop canopy develops. Generally, the warming effect has a good promotion effect on crop growth, but the crop growth is hampered even with a yield reduction when the increased soil temperature caused by PM exceeds the tolerant temperature for plant growth. Moreover, the compensatory effect of PM could be used to quantify the growth and development of crops under PM and has been widely applied to cotton, corn, winter wheat, and rice. The compensation coefficient is larger in the early stage of crop growth than in the later stage. The compensation coefficient has certain differences for the same crop because of the influence of climate factors, soil moisture content, and soil microtopography. In future research, the theoretical integration of the safety period of PM and the time threshold of the compensatory effect could be theoretically interpreted, and the construction of the compensatory effect module in the crop models will also be an important issue. [ABSTRACT FROM AUTHOR]

An efficient and accurate estimation of wheat growth and yield is important for wheat assessment and field management. To improve the accuracy and stability of wheat growth and yield estimation, an estimation method based on a genetic algorithm-improved support vector regression (GA-SVR) algorithm was proposed in this study. The correlation analysis between vegetation indices calculated from spectral data and wheat growth phenotypes and yields was performed to obtain the optimal combination of vegetation indices with high correlation and good estimation performance. At the same time, the optimal model for wheat growth monitoring was screened and constructed in experiments with 12 wheat varieties and 3 gradient nitrogen fertilizer application levels. Then, the yield estimation model was established and its applicability was verified under different nitrogen fertilizer application levels. The results showed that the constructed models for the leaf area index, plant height, and yield estimation performed well, with coefficients of determination of 0.82, 0.71, and 0.70, and root mean square errors of 0.09, 2.7, and 68.5, respectively. This study provided an effective UAV remote sensing technique for monitoring wheat growth status and estimating yield. This study provides an effective unmanned aerial remote sensing technique for monitoring wheat growth and estimating yield, and provides technical support for wheat yield assessment and field management. [ABSTRACT FROM AUTHOR]

This study, employing the AquaCrop model, demonstrated notable efficacy in assessing and predicting crop yields for winter wheat, maize, winter rapeseed, and sugar beets in the Joint Experiment for Crop Assessment and Monitoring (JECAM) test area of Poland from 2018 to 2023. In-situ measurements, conducted through field campaigns, included parameters such as electromagnetic radiation reflectance, Leaf Area Index (LAI), soil moisture, accumulated photosynthetically active radiation, chlorophyll content, and plant development phase. The model was calibrated with input data covering daily climatic parameters from the ERA5-land Daily Aggregated repository, crop details, and soil characteristics. Specifically, for winter wheat, the Root Mean Square Error (RMSE) values ranged from 1.92% to 14.26% of the mean yield per hectare. Maize cultivation showed RMSE values ranging from 0.21% to 1.41% of the mean yield per hectare. Winter rapeseed exhibited RMSE values ranging from 0.58% to 17.15% of the mean yield per hectare. In the case of sugar beets, the RMSE values ranged from 0.40% to 1.65% of the mean yield per hectare. Normalized Difference Vegetation Index (NDVI)-based predictions showed higher accuracy for winter wheat, similar accuracy for maize and sugar beets, but lower accuracy for winter rapeseed compared to Leaf Area Index (LAI). The study contributes valuable insights into agricultural management practices and facilitates decision-making processes for farmers in the region. [ABSTRACT FROM AUTHOR]

Subsoiling tillage breaks up the shallow plow layer and thickened plow pan resulting from prolonged crop rotation, thus enhancing the soil tillage layer environment and fostering crop growth. However, these changes in tillage practices are not accompanied by corresponding advancements in irrigation technology. Therefore, this study compared drip irrigation (DI) and micro-sprinkler irrigation (MS) with three watering levels (H, M, L) based on soil water content (70%, 60%, 50% of field capacity) against traditional surface irrigation (CK, 70%FC) to find the most suitable irrigation approach for subsoiling wheat fields. This study found that adjusting irrigation methods and regimes significantly impacted wheat growth and yield. Drip irrigation boosts winter wheat grain yield, harvest index, biomass transfer amount, biomass transfer rate, nitrogen accumulation, nitrogen use efficiency, and nitrogen harvest index significantly compared to surface and micro-sprinkler methods. Drip irrigation, notably the DI-M treatment, significantly enhances winter wheat grain yield by 28.7% compared to CK. Drip irrigation produced optimal results when soil water levels decreased to 60% of the field capacity. This suggests adopting a combination of DI, with irrigation initiated at 60% of field capacity, for enhanced wheat production and resource efficiency. [ABSTRACT FROM AUTHOR]

Supply chain disruptions, a pandemic, and war in Ukraine have exposed faultlines in a globalised food system that depends on a few staple crops grown in a few exporting regions and transported to consumers around the world. In the UK, just three crops, (wheat, barley, and oilseed rape), account for 75 per cent of the UK's 4.5 million hectares of arable land whilst the country imports around half its food—nearly 40 per cent—from just four EU countries (The Netherlands, Ireland, Germany, and France). Poor diets contribute to one in seven deaths in the UK, 63 per cent of the population is overweight or obese and health inequality is increasing between the poorest and most affluent regions. The food security and health of the UK population is therefore dependent on a small number of locally grown crops, vulnerable supply chains, and an unhealthy, obesogenic diet. The UK food system must diversify if it is to become food and nutritionally secure, meet its climate and biodiversity goals and have a healthy and active population. Climate-resilient and nutritious underutilised crops can help diversify the UK agrifood system, but research and investment in them is sporadic, piecemeal, and unfocused. In this paper, we compare two approaches to identifying potentially suitable underutilised crops for the UK. The first, based on UK Department for Environment, Food and Rural Affairs (Defra) Project CH0224, was delivered through literature and database searches and the expertise of growers, advisers, breeders, seed suppliers, processors, traders, and researchers. The second used the CropBASE digital knowledge base for underutilised crops. The two approaches produced no single crop that was common to both shortlists. We propose that the analytical and predictive tools derived from CropBASE could be combined with local knowledge and expertise from the Defra project to provide a common framework for the identification of underutilised crops that are best suited to local UK circumstances now and in climates of the future. [ABSTRACT FROM AUTHOR]

In order to eliminate the limitations of traditional winter wheat yield prediction methods, the prediction models based on machine learning are used to improve the accuracy of winter wheat yield prediction. In this study, by collecting a large amount of domestic literature about wheat growth characteristics, the irrigation amount, fertilization amount, soil nutrient status, planting density, maximum leaf area index (LAImax), maximum aboveground dry matter accumulation (Dmax) and yield (Y) were chosen to develop the learning models. Using the data of the irrigation amount, fertilization amount, soil nutrient status and planting density as the training set, the regression prediction models (Gaussian process regression mode, linear regression model, regression tree mode and support vector machine model) were used to train and learn the data of the LAImax, Dmax and Y, respectively. The results show that the Gaussian regression model has the best precision compared to the other models. The coefficients of determination (R2) of the learning results of the Gaussian regression model for the LAImax, Dmax and Y are 0.9, 0.93 and 0.86, and the root mean square error (RMSE) is 0.57, 1125.1 and 640.41. Based on the data of the irrigation amount, nitrogen application amount, potassium application amount, phosphorus application amount, organic matter content, total nitrogen content, alkali-hydrolyzable nitrogen content, available phosphorus content, available potassium content and planting density, the method proposed in this paper can reliably predict the LAImax, the Dmax and Y of winter wheat. The results also have certain reference significance for the yield prediction of other crops. [ABSTRACT FROM AUTHOR]

Drought is the most important factor limiting winter wheat yield in the North China Plain (NCP). Choosing high-yielding cultivars is an important measure to minimize the negative effects of drought stress. Field studies were conducted with 10 cultivars in the 2020–2022 seasons under three irrigation treatments (I0, without irrigation; I1, irrigated at jointing stage; I2, irrigated at jointing and anthesis stages) in the NCP to examine the water use strategies and root and shoot traits of high-yielding cultivars under different water supply conditions. The results showed that yield variation among cultivars was 21.2–24.6%, 23.7–25.9% and 11.6–15.3% for the I0, I1 and I2 treatments, respectively. Under water deficit conditions (I0 and I1), high-yielding cultivars reduced water use during vegetative stages and increased soil water use during reproductive stages, especially water use from deeper soil layers. Those cultivars with higher root length density (RLD) in deep soil layers exhibited higher water uptake. Each additional millimeter of water used after anthesis from the 100–200 cm soil layers increased grain yield by 23.6–29.6 kg/ha and 16.4–28.5 kg/ha under I0 and I1, respectively. This water use strategy enhanced dry matter accumulation after anthesis, decreased canopy temperature (CT) and increased relative leaf water contents (RLWC), which ultimately improved grain yield. For winter wheat grown under I2, cultivars that decreased water use after anthesis had higher water productivity (WP). Root length (RL), root weight (RW) and root:shoot ratio were each negatively correlated with grain yield, while above-ground biomass was positively correlated with grain yield. Therefore, higher dry matter accumulation and smaller root systems are two important traits of high-yielding cultivars under sufficient water supply conditions (I2) in the NCP. [ABSTRACT FROM AUTHOR]

The application of nitrogen (N) improves the winter wheat yield. Excessive N application affects winter wheat yields, leading to low net incomes and negative environmental impacts, therefore, optimizing N application is essential. In this study, the effects of N rates on crop growth yield, net income (NI), water use efficiency (WUE), and nitrogen use efficiency (NUE) in the irrigated districts of the eastern loess plateau, China, were investigated using seven N application rates (N0, N90, N180, N210, N240, N270, and N300 kg ha−1) during the 2016–2017 and 2017–2018 seasons. N application significantly increased the total water consumption at 0–200 cm during the growth period, the aboveground dry matter at maturity, the grain nitrogen accumulation, yield, NI, and WUE. However, N exceeding 240 kg ha−1 did not favor dry matter and nitrogen accumulation or translocation from the anthesis stage to the maturity stage, thus leading to reduced yield, NI, and WUE. The transpiration rate and stomatal conductance N240 was highest 21–28 day after anthesis, at 187–276 kg ha−1, which can achieve a high yield and profitability with relatively low environmental costs. [ABSTRACT FROM AUTHOR]

Knowledge of the responses of winter wheat yield to meteorological dryness/wetness variations is crucial for reducing yield losses in Henan province, China's largest winter wheat production region, under the background of climate change. Data on climate, yield and atmospheric circulation indices were collected from 1987 to 2017, and monthly self-calibrating Palmer drought severity index (sc-PDSI) values were calculated during the winter wheat growing season. The main results were as follows: (1) Henan could be partitioned into four sub-regions, namely, western, central-western, central-northern and eastern regions, based on the evolution characteristics of the time series of winter wheat yield in 17 cities during the period of 1988–2017. Among them, winter wheat yield was high and stable in the central-northern and eastern regions, with a remarkable increasing trend (p < 0.05). (2) The sc-PDSI in February had significantly positive impacts on climate-driven winter wheat yield in the western and central-western regions (p < 0.05), while the sc-PDSI in December and the sc-PDSI in May had significantly negative impacts on climate-driven winter wheat yield in the central-northern and eastern regions, respectively (p < 0.05). (3) There were time-lag relationships between the sc-PDSI for a specific month and the atmospheric circulation indices in the four sub-regions. Furthermore, we constructed multifactorial models based on selected atmospheric circulation indices, and they had the ability to simulate the sc-PDSI for a specific month in the four sub-regions. These findings will provide scientific references for meteorological dryness/wetness monitoring and risk assessments of winter wheat production. [ABSTRACT FROM AUTHOR]

Septoria tritici blotch (STB) ais one of the most damaging winter wheat diseases worldwide, presenting a significant threat to its yields. The causal STB agent, Zymoseptoria tritici, also presents a challenge to control due to its rapid adaptation to fungicides. This requires researchers to continuously monitor the pathogen and investigate and explore strategies to manage the spread of the disease and the development of resistance in the pathogen. Therefore, this study presents the current situation and describes changes in the sensitivity of Z. tritici isolates from Lithuania to quinone outside inhibitors (QoIs) and succinate dehydrogenase inhibitors (SDHIs) for the years 2019–2022. The isolates were tested at five different concentrations of two QoI fungicides (azoxystrobin and pyraclostrobin) and three SDHI fungicides (fluxapyroxad, benzovindiflupyr, and bixafen). During the test period, the EC50 values of the tested QoIs increased, while no clear changes were observed in the SDHIs. The most pronounced shift was observed for the active QoI substance pyraclostrobin. The distribution of the EC50 values of the SDHI fungicides showcased one isolate with an outstandingly high EC50 value of 2.6 mg L−1. The results of this study did not reveal any strong patterns of cross-resistance between the fungicides tested. However, a significant positive, moderate correlation (r = 0.55) was found between fluxapyroxad and benzovindiflupyr. Overall, the results of this study contribute to the understanding of the fungicide-resistance situation of Z. tritici in Lithuania and may complement management strategies for the pathogen and its fungicide resistance. [ABSTRACT FROM AUTHOR]

Intensive wheat production, which produces high yields through the excessive use of chemical inputs and non-renewable energy, is unsustainable in the long term. Innovative cultivation methods such as intercropping can address emerging challenges. This kind of plant association offers the possibility of achieving a balanced yield with the use of a natural nitrogen source. An experiment was conducted for three growing seasons (2020/2021, 2021/2022, 2022/2023) with a combination of three winter wheat varieties (GK Szilárd, Cellule, GK Csillag) and a winter pea variety (Aviron) in three sowing densities to determine the species interaction and the economics of mixed plots. The intercropping systems were evaluated in terms of the land equivalent ratio (LER), aggressivity (A), competitive ratio CR), actual yield loss (AYL), monetary advantage index (MAI), and intercropping advantage (IA). In almost all mixtures, the values of partial A, CR, and AYL indicated that wheat was more competitive than peas due to the overconcentration of mixtures. For MAI, the mixture Cellule/Aviron 75:50 was more profitable than the others in the first two years. Our results draw attention to the influence of the seeding rate, which can contribute to new directions for current research. [ABSTRACT FROM AUTHOR]

This study examines the significant variability in grain yield, thousand-grain weight, protein content, sedimentation value, and falling number among winter wheat cultivars across diverse trial locations, elucidating the profound influence of environmental factors on these traits. Employing Shukla's stability variance and a multi-trait stability index (MTSI), cultivar stability is comprehensively assessed across multiple traits. Cultivars are ranked based on stability variance and cumulative ranking across all traits, with Bataja emerging as the most stable cultivar according to Shukla variance, while Apostel exhibits the lowest stability. Contrarily, MTSI rankings reveal distinct top performers, such as Medalistika and KWS Spencer. Canonical correspondence analysis (CCA) is utilized to discern relationships between stability and genotype characteristics, as well as trait values and soil properties/weather conditions. These findings contribute to the recommendation of stable cultivars for breeding programs and the optimization of crop management practices. Furthermore, this study underscores the need to explore causal relationships between accompanying variables, facilitating informed recommendations for plant breeders and advancing breeding progress amidst a changing climate. The use of multivariate statistical methods, including CCA, enhances our understanding of cultivar traits and stability, offering valuable insights for sustainable agriculture. [ABSTRACT FROM AUTHOR]

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran's I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought. [ABSTRACT FROM AUTHOR]

In the aspect of the sustainable development of agrocenoses, the proper management of plant residues remaining after harvesting, the appropriate selection of tillage and maintaining high microbiological activity of soil are particularly important. Therefore, a four-year two-factor experiment with winter wheat monoculture was conducted. The objective of this study was to compare the effects of 18 cultivation technologies variants on weed infestation and yield structure of winter wheat grown in a 4-year monoculture. Six methods of tillage and management of residues after harvesting forecrops (first factor) and the use of microbiological preparations (second factor) were tested. The experiment showed that simplified tillage (elimination of plowing) had an adverse effect on the weed infestation of the field and most of the tested plant characteristics, including the yield. In terms of yield, the best solution was to leave the forecrop straw mulch on the field surface until plowing was carried out before sowing, regardless of the use of microbiological preparations. The application of preparations containing effective microorganisms brought beneficial effects only when the shredded straw of the forecrop was mixed with the soil using a grubber. [ABSTRACT FROM AUTHOR]

Drought stress, a major abiotic stress, significantly affects wheat (Triticum aestivum L.) production globally. To identify genes and metabolic pathways crucial for responding to short-term drought stress, we conducted transcriptomic and metabolomic analyses of winter wheat cultivar Jimai 418 at four developmental stages: jointing (GS31), booting (GS45), anthesis (GS65), and 8 days after anthesis (DAA8). Transcriptomic analysis identified 14,232 differentially expressed genes (DEGs) under drought stress compared to the control. Specifically, 1387, 4573, 7380, and 892 DEGs were identified at the four developmental stages, respectively. Enriched pathways associated with these DEGs included plant hormone signal transduction, mitogen-activated protein kinase (MAPK) signaling, galactose metabolism, and starch and sucrose metabolism. Totals of 222, 633, 358, and 38 differentially accumulated metabolites (DAMs) were identified at the four stages, respectively. Correlation analysis of both datasets revealed DEGs and DAMs associated with plant hormone signal transduction, arginine and proline metabolism, ABC transporters, and amino acid biosynthesis. These findings offer significant insights into Jimai 418's molecular response to short-term drought stress. The identified DEGs, DAMs, and enriched pathways contribute to our understanding of wheat drought tolerance. This research will facilitate further investigations into drought tolerance mechanisms and guide the breeding of wheat varieties with enhanced drought resistance. [ABSTRACT FROM AUTHOR]

Drought stress is the primary abiotic factor affecting wheat growth, development, and yield formation. The application of plant growth-promoting rhizobacteria (PGPR) represents an environmentally sustainable approach to mitigate the impacts of drought stress on wheat. This study conducted field experiments using two winter wheat varieties, the drought-sensitive variety Jimai 22 and the drought-resistant variety Chang 6878, aiming to investigate the effects of Streptomyces pactum Act12 inoculation on photosynthetic characteristics, physiological parameters, and yield traits during the jointing, heading, and middle-filling stages under drought stress. The results revealed that drought stresses significantly reduced chlorophyll content, leaf area, biomass, and yield in wheat, while Act12 inoculation significantly increased chlorophyll content, photosynthetic efficiency, antioxidant enzyme activity such as superoxide dismutase (SOD) and peroxidase (POD), osmolyte content (proline and soluble proteins), and decreased malondialdehyde (MDA) content. These combined effects alleviated drought stress, resulting in increased biomass and yield in wheat. Under drought stress, an increase in leaf proline content of 13.53% to 53.23% (Jimai 22) and 17.17% to 43.08% (Chang 6878) was observed upon Act12 inoculation. Moreover, a decrease in MDA content was recorded of 15.86% to 53.61% (Jimai 22) and 13.47% to 26.21% (Chang 6878). Notably, there was a corresponding increase in yield of 11.78% (Jimai 22) and 13.55% (Chang 6878). In addition, grain quality analysis revealed a significant improvement in grain hardness with Act12 inoculation. Therefore, Act12 demonstrates the potential for enhancing the sustainable development of wheat production in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

This document explores the impact of nitrogen fertilizer on crop yields and quality in winter wheat and potatoes. It found that increasing rates of nitrogen fertilizer resulted in higher grain yields and improved protein and gluten content in winter wheat. Late-season nitrogen application was also found to be important for wheat yield and quality. In potatoes, the use of sulfur in combination with nitrogen fertilizer increased tuber yield and nitrogen use efficiency. The document also discusses the role of inorganic nitrogen in soil and its impact on greenhouse gas emissions. The findings emphasize the importance of optimizing fertilizer application to maximize crop productivity while minimizing environmental impacts. [Extracted from the article]

The English grain aphid (EGA) (Sitobion avenae F.) is one of the most destructive species of aphids in wheat- (Triticum aestivum L.) planting areas worldwide. Large quantities of insecticides are usually used to control aphid damage. The identification of new EGA-resistant genes is necessary for sustainable wheat production. The objective of this study was to identify candidate genes for EGA resistance from stable quantitative trait loci (QTLs). We previously constructed a genetic map of unigenes (UG-Map) with 31,445 polymorphic sub-unigenes via the RNA sequencing of 'TN18 × LM6' recombinant inbred lines (TL-RILs). The relative aphid index (RAI) for the TL-RILs was investigated for two growing seasons, with three measured times (MTs) in each season. Using the UG-Map, 43 candidate genes were identified from 22 stable QTLs, with an average of 1.95 candidate genes per QTL. Among the 34 candidate genes annotated in the reference genome Chinese Spring (CS) RefSeq v1.1, the homologous genes of seven candidate genes, TraesCS1A02G-319900, TraesCS1B02G397300, TraesCS2D02G460800, TraesCS4A02G015600LC, TraesCS5B02G329200, TraesCS-6A02G000600 and TraesCS6A02G418600LC have been previously reported to play roles in aphid resistance. This suggests that these genes are strongly associated with EGA resistance in wheat. The candidate genes in this study should facilitate the cloning of EGA-resistant genes and genetic improvement in wheat breeding programs. [ABSTRACT FROM AUTHOR]

Due to increasingly frequent unfavorable climate changes, achieving a high grain yield of wheat is a challenge for breeders. The relationships between wheat productivity and photosynthesis traits are not very well understood during the growing season. This study investigated the effect of chlorophyll a fluorescence parameters (maximum quantum yield of primary photochemistry (TRO/ABS) and performance index on absorption basis (PIABS)) on grain yield and the yield-related and technological quality traits of six wheat varieties over two growing seasons. In the first growing season (2021/2022), grain yield was significantly positively correlated with 1000 kernel weight and TRO/ABS at the second measurement point (growth stage 25 (GS 25)). Only the highest-yielding variety Bubnjar (104.0 dt ha−1) showed values of TRO/ABS at the same significance level between the second and third measurement points. Due to elevated virus and disease infections in the second growing season (2022/2023), the grain yield of the investigated varieties decreased between 37.9% (Bubnjar) and 67.6% (Anđelka) relative to the first growing season. The three highest-yielding varieties (Bubnjar, Rujana, and Silvija) in 2022/2023 were the tallest, were later in maturity, escaped yellow rust pressure at the stem elongation stage more efficiently, and also showed the lowest increase in TRO/ABS at this stage (fourth measurement point at GS 47, compared to the third at GS 32). In addition, the highest-yielding variety Bubnjar showed the highest increase in PIABS at the last measurement (seventh) at GS 71 compared to the sixth (GS 69), thus maintaining the vitality of flag leaves at the grain-filling stage, while the other varieties showed a very small increase or even a significant decrease. Therefore, plant photosynthetic activity over the entire growing season contributes to crop productivity. [ABSTRACT FROM AUTHOR]