Estimation of crop residue cover is important for energy balance in agroecosystem and sustainable development of agriculture. We evaluated the dimidiate pixel model, widely used for estimating photosynthetic vegetation cover, for non-photosynthetic vegetation (such as winter wheat residue) cover estimation. In this study, based on spectral and cover data of winter wheat residue in dry and wet soil backgrounds, the spectral curves of winter wheat residue and soil were identified, the applicability of non-photosynthetic vegetation indices in dimidiate pixel model was analyzed, and the potential of dimidiate pixel model to estimate winter wheat residue cover was explored. In dry soil background, a lignocellulose absorption trough near 2100 nm in the spectral curve of residue-soil mixed scene was observed, and the absorption trough became deeper with increasing residue cover. The normalized difference tillage index (NDTI) had the best correlation with the measured cover of winter wheat residue, and the dimidiate pixel model constructed on the basis of this index was able to accurately estimate the winter wheat residue cover (R2=0.64, RMSE=0.16, RE=26.32 %). In wet soil background, the ability of non-photosynthetic vegetation index to distinguish between winter wheat residue and soil was reduced by soil moisture. The results of this study provide effective insights into the estimation of winter wheat residue cover under different soil moisture conditions, and provide a useful reference for the study of remote sensing estimation of crop residue cover in a large region. The dimidiate pixel model using NDTI can also be used to estimate non-photosynthetic vegetation cover of natural vegetation.

Winter wheat (Triticum aestivum L.) is one of the most important staple food crops worldwide and commonly grown in crop rotations to obtain high grain yields and overcome increased disease pressure which is caused by monoculture cultivation. However, based on the high economic advantages of wheat production, in several cases wheat is also grown after wheat (stubble wheat or monoculture). It is hypothesized that lower grain yield of wheat, grown after wheat is caused by differences in the root development of the plants, which might be triggered by changes in the soil structure or an infection with the fungus Gaeumannomyces graminis var. tritici (Ggt). This thesis investigated the effects of the crop rotational position of winter wheat on soil structure at the beginning of the growing season, possible differences in wheat biomass formation at this timepoint, as well as the effects on above-ground biomass formation, root development and nutrient acquisition during the growing season, a possible take-all occurrence, and, finally, wheat grain yield. The root development was investigated with destructive soil core sampling at two timepoints and with biweekly scans of minirhizotron tubes throughout the growing season. The third study further aimed to adapt an existing Convolutional neural network (CNN)-based segmentation method for high-throughput image analysis of minirhizotron images of wheat as well as to investigate a possible effect of the presence of the minirhizotron tubes in the soil environment on root development. The crop rotational position of winter wheat did not cause differences in soil structure in April and therefore was not related to differences in wheat biomass formation by this date. Root length density in subsoil at later growth stages was higher for winter wheat grown after oilseed rape compared to after winter wheat, which was independent of take-all infection and corresponded well to a higher biomass formation and finally a higher grain yield. The root development of winter wheat, especially in the subsoil, might thus be the key to understand biomass and yield formation of wheat in different crop rotational positions. The minirhizotron image analysis based on the adapted CNN showed that the presence of the minirhizotron tubes affect the root development only if the tubes are not installed with a good soil-tube contact, which can be achieved by slurrying. Further investigations are needed to identify processes, related to biomass formation of winter wheat cultivated after winter wheat pre-crop in detail and which properties of the pre-crops affect the root and overall biomass development of the subsequent crop. Possibly, research on the rhizosphere microbiome and the related processes, also in the early development phase of the plants, can present beneficial genera of bacteria which might be able to overcome wheat yield decline. | 2025-02-21

Chlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter wheat. Derivative preprocessing has a wide range of applications in the hyperspectral monitoring of winter wheat chlorophyll. In order to research the role of fractional-order derivative (FOD) in the hyperspectral monitoring model of ChD, this study based on an irrigation experiment of winter wheat to obtain ChD and canopy hyperspectral reflectance. The original spectral reflectance curves were preprocessed using 3 FOD methods: Grünwald-Letnikov (GL), Riemann-Liouville (RL), and Caputo. Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). The main results were as follows: For the 3 types of FOD, GL-FOD was suitable for analyzing the change process of the original spectral curve towards the integer-order derivative spectral curve. RL-FOD was suitable for constructing the hyperspectral monitoring model of winter wheat ChD. Caputo-FOD was not suitable for hyperspectral research due to its insensitivity to changes in order. The 3 FOD calculation methods could all improve the correlation between the original spectral curve and Log(ChD) to varying degrees, but only the GL method and RL method could observe the change process of correlation with order changes, and the shorter the wavelength, the smaller the order, and the higher the correlation. The bands screened by CARS were distributed throughout the entire spectral range, but there was a relatively concentrated distribution in the visible light region. Among all models, CARS was used to screen bands based on the 0.3-order RL-FOD spectrum, and the model constructed using ETsR reached the best accuracy and stability. Its R2c, RMSEc, R2v, RMSEv, and RPD were 1.0000, 0.0000, 0.8667, 0.1732, and 2.6660, respectively. In conclusion, based on the winter wheat ChD data set and the corresponding canopy hyperspectral data set, combined with 3 FOD calculation methods, 1 band screening method, and 8 modeling algorithms, this study constructed hyperspectral monitoring models for winter wheat ChD. The results showed that based on the 0.3-order RL-FOD, combined with the CARS screening band, ETsR modeling has the highest accuracy, and hyperspectral estimation of winter wheat ChD can be realized. The results of this study can provide some reference for the rapid and nondestructive estimation of ChD in winter wheat.

Global warming is primarily characterized by asymmetric temperature increases, with greater temperature rises in winter/spring and at night compared to summer/autumn and the daytime. We investigated the impact of winter night warming on the top expanded leaves of the spring wheat cultivar Yangmai 18 and the semi-winter wheat cultivar Yannong 19 during the 2020–2021 growing season. Results showed that the night-time mean temperature in the treatment group was 1.27°C higher than the ambient temperature, and winter night warming increased the yields of both wheat cultivars, the activities of sucrose synthase and sucrose phosphate synthase after anthesis, and the biosynthesis of sucrose and soluble sugars. Differentially expressed genes (DEGs) were identified using criteria of P-value<0.05 and fold change>2, and they were subjected to Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Genes differentially expressed in wheat leaves treated with night warming were primarily associated with starch and sucrose metabolism, amino acid biosynthesis, carbon metabolism, plant hormone signal transduction, and amino sugar and nucleotide sugar metabolism. Comparisons between the groups identified 14 DEGs related to temperature. These results highlight the effects of winter night warming on wheat development from various perspectives. Our results provide new insights into the molecular mechanisms of the response of wheat to winter night warming and the candidate genes involved in this process.

Abstract Wheat and barley serve as significant nutrient-rich staples that are extensively grown on a global scale, spanning over 219 million hectares. The annual combined global yield is 760.9 million tons, with Kazakhstan contributing 14.3 million tons of wheat and 3.83 million tons of barley to this total. The productivity of grain crops has declined annually due to fungal disease, especially root and crown rot caused by Bipolaris sorokiniana and Fusarium spp. Research has focused on pinpointing the pathogens responsible for common root rot in various types of wheat and barley grown in Southeast Kazakhstan. The main goal was to examine the efficacy of certain chemical and biological substances in safeguarding barley seedlings during the early growth stage against root rot root rot. Moreover, this study sought to gauge their effects on seed quality by examining aspects such as germination rates, the colonization of seeds by particular fungal pathogens, and the overall vitality of seeds and seedlings. Visual inspection of the plants revealed that the prevalence of B. sorokiniana was an average of 51.8%, and that of Fusarium species was 58.6%. Three isolates were obtained from the roots of the winter wheat promising line 231, three from the spring wheat roots of the Kazakh variety 10, four from the winter wheat variety Steklovidnaya variety 24, fourteen from the spring barley variety Symbat, and fourteen from the winter barley variety Aidyn variety 2. The external spread of common root rot on spring wheat and spring barley varieties reached 50% and 53%, respectively. Promising line 231 of winter wheat and variety Kazakh 10 of spring barley were affected by the disease by 60%, whereas the winter wheat Steklovidnaya 24 was impacted by 67%. Molecular analysis of B. sorokiniana isolates via species-specific primers (COSA_F/COSA_R) from infected plant tissues confirmed their identification. Koch postulates were fulfilled for B. sorokiniana isolates Kz 48, 60, and 82 on Steklovidnaya 24 winter wheat and Symbat spring barley varieties. Biological products such as Phytosporin-M and Sporobacterin-Rassada significantly reduced the level of fungal infection, confirming their potential as environmentally safe plant protection agents.

It is crucial to elucidate the impact of climate change on wheat production in China. This article provides a review of the current climate change scenario and its effects on wheat cultivation in China, along with an examination of potential future impacts and possible response strategies. Against the backdrop of climate change, several key trends emerge: increasing temperature during the wheat growing season, raising precipitation, elevated CO2 concentration, and diminished radiation. Agricultural disasters primarily stem from oscillations in temperature and precipitation, with the northern wheat region being mostly affected. The impact on wheat production is manifested in a reduction in the area under cultivation, with the most rapid reduction in spring wheat, and a shift in the center of cultivation to the west. Furthermore, climate change accelerates the nutritional stage and shortens phenology. Climate change has also led to an increase in yields in the Northeast spring wheat region, the Northern spring wheat region, the Northwest spring wheat region, and the North China winter wheat region, and a decrease in yields in the middle and lower reaches of the Yangtze River winter wheat region, the Southwest winter wheat region, and the South China winter wheat region. To cope with climate change, Chinese wheat can adopt adaptation strategies and measures such as breeding different wheat varieties for different wheat-growing regions, implementing differentiated farmland management measures, promoting regional ecological farmland construction, and establishing scientific monitoring and early warning systems. While future climate change may stimulate wheat yield potential, it could cause climate-induced issues such as weeds, diseases, and pests worsen, thereby posing challenges to the sustainability of farmland. Moreover, it is essential to conduct comprehensive research on pivotal areas such as the microscopic mechanism of climate change and wheat growth, the comprehensive influence of multiple climate factors, and the application of new monitoring and simulation technologies. This will facilitate the advancement of related research and provide invaluable insights.

The nitrogen nutrition index (NNI) is a significant agronomic statistic used to assess the nitrogen nutrition status of crops. The use of remote sensing to invert it is crucial for accurately diagnosing and managing nitrogen nutrition in crops during critical periods. This study utilizes the UHD185 airborne hyperspectral imager and the ASD Field Spec3 portable spectrometer to acquire hyperspectral remote sensing data and agronomic parameters of the winter wheat canopy during the nodulation and flowering stages. The objective is to estimate the NNI of winter wheat through a winter wheat nitrogen gradient experiment conducted in Leling, Shandong Province. The ASD spectral reflectance data of the winter wheat canopy were selected as the reference standard and compared with the UHD185 hyperspectral data obtained from an unmanned aerial vehicle (UAV). The comparison focused on analyzing the trends in the spectral curve changes and the spectral correlation between the two datasets. The findings indicated a strong agreement between the UHD185 hyperspectral data and the spectral data obtained by ASD in the range of 450–830 nm. A spectrum index was developed to estimate the nitrogen nutritional index utilizing the bands within this range. The linear model, based on the first-order derivative ratio spectral index (RSI) (FD₆₆₆, FD₈₂₆), demonstrated the highest accuracy in estimating the nitrogen nutrient index in winter wheat. The model yielded <i>R</i>² values of 0.85 and 0.75, respectively, and may be represented by the equation <i>y</i> = −2.0655<i>x</i> + 0.156. The results serve as a benchmark for future utilization of the UHD185 hyperspectral data in estimating agronomic characteristics of winter wheat.

In order to investigate the changes in winter wheat yield and the factors influencing it, five meteorological factors—such as rainfall and soil moisture—collected from the experimental area between 2010 and 2022 were used as characteristic features. A combined model of GNN (Graph Neural Network), based on the Fourier transform and the Random Forest algorithm was developed to predict winter wheat yield. Matrix multiplication in Fourier space was performed to predict yield, while the Random Forest algorithm was employed to quantify the contribution of various yield factors to winter wheat yield. The combined model effectively captured the dynamic dependencies between yield factors and time series, improving predictive accuracy by 5.00%, 10.00%, and 27.00%, and reducing the root mean square error by 26.26%, 29.31%, and 88.20%, respectively, compared to the StemGNN, Informer, and Random Forest models. The predicted outputs ranged from 520 to 720 g/m², with an average error of 2.69% compared to the actual measure outputs. Under the insufficient real-time irrigation mode, winter wheat yield was highest at 90% irrigation upper limit and 70% irrigation lower limit, with a medium fertilization level (850 mg/kg). The yield showed an overall decreasing trend as both irrigation limits and fertilizer application decreased. Rainfall and soil moisture were the most significant factors influencing winter wheat yield, followed by air temperature and evapotranspiration. Solar radiation and sunshine duration had the least impact. The results of this study provide a valuable reference for accurately predicting winter wheat yield.

The study was aimed at exploring the potential of ethylmethanesulfonate (EMS) as a mutagen by examining its ability to induce chromosomal aberrations, its interaction with different genotypes, and the characteristics of genotype-mutagenic interactions. It also focused on evaluating the feasibility of using EMS in future applications, including its predictive value when tested at the cellular level for determining its mutation-inducing capacity at the plant level. Seeds of winter wheat two varieties (Spivanka and Altigo) were treated with ethylmethansulfonate (EMS) at concentrations of 0.025%, 0.05%, and 0.1%, and sodium azide (SA) at concentrations of 0.01%, 0.025%, 0.05%, and 0.1%. The study of cytogenetic activity, evaluated through pollen sterility and the frequency and spectrum of chromosomal abnormalities in mid-phase cell mitosis, revealed significant findings regarding genotype-mutagenic interactions in the wheat varieties. Genotype-mutagenic interactions are crucial in determining variability in chromosomal aberrations. The wheat variety Altigo demonstrated significantly higher genotype-mutagenic specificity, making it a promising candidate for inducing variability and obtaining mutant forms. Altigo showed particularly effective responses when treated with EMS and SA concentrations ranging 0.025% to 0.05%, which optimized the mutagenic effects without excessive adverse impacts. The study underscores critical findings of the parameters that define the genetically determined susceptibility of wheat varieties to ecogenetic factors, particularly focusing on the mutagens EMS and SA. The pollen fertility, overall frequency of chromosomal aberrations, and number of induced fragments were observed to be the most reliable indicators of genetic susceptibility to mutagens. Other parameters, particularly rare chromosomal rearrangements, only partially reflected the trends or failed to provide meaningful data, indicating limited utility in such analyses. The agents under study exhibited induction patterns consistent with those observed for other chemical supermutagens, although variations occured based on the initial genetic material of the plant. The data will be integrated with studies on the frequency and quality of resulting hereditary changes, particularly in complex biochemical and physiological traits. These results provide a foundation for refining mutagenic strategies and identifying optimal conditions and materials for future breeding programs.

Precise and timely mapping of winter wheat is essential for food security. Current methods are limited by insufficient training data and a lack of long-term early mapping verification. This research proposes a framework that uses a cascade index to generate high-quality training samples for winter wheat mapping automatically. By considering the phenological characteristics of winter wheat and similar crops, the cascade index method screens and acquires these samples. Combined with a random forest model, mapping was conducted in Henan Province and the Agricultural Statistics District (ASD) 2020 area in the U.S. In Henan, early mapping from 2018 to 2022 assessed differences between model transfer and current-year samples. Results showed that using October-April imagery based on model migration achieved an overall accuracy (OA) of over 90%, while October-February imagery based on current-year samples also exceeded 90%. In some years, early mapping using only October-December data also achieved over 90% OA. These findings demonstrate the proposed model's viability for large-scale early winter wheat mapping using satellite imagery. Furthermore, this method demonstrates adaptability, mapping results achieving over 93.69% OA when transferred to the United States.