Search Results (349)

Since wheats used for use in brewing mainly belong to the winter red hard hybrid endosperm type, this paper examined the influence of different proportions of wheat of this type (seven varieties) in the ratio of 0–100% in the grist, both unmalted and as wheat malt. The quality of the starting wheats, the resulting malts and mashs with different added wheat proportions (100, 80, 60, 40, 20 and 0%) were examined. The obtained results show that the maximum shares of wheat/wheat malt in the infusion are significantly different between varieties of similar initial quality. However, they can differ considerably for the same variety when it is used as unmalted raw material and when it is used as wheat malt. Wheat malt can be added to the mixture in a significantly larger proportion compared to unmalted wheat. Furthermore, when an extended number of criteria (parameters) are applied, some varieties may be acceptable that otherwise would not be if the basic number of parameters were applied (total protein—TP, total soluble protein—TSP and viscosity—VIS) and vice versa. The inclusion of other parameters—filtration speed (FIL), saccharification time (SAC), color (COL), proportion of fine extract (EXT) and fermentability of pomace (FAL) (some of which have the character of so-called “cumulative parameters”)—complicates a clear classification into the aforementioned qualitative groups but also increases the number of varieties acceptable or conditionally acceptable for brewing. Full article

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

The review summarizes the historical and current research on perennial grain breeding in Russia within the context of growing global interest in perennial crops. N.V. Tsitsin’s pioneering work in the 1930s produced the first wheat–wheatgrass amphiploids, which demonstrated the capacity to regrow after harvest and survive for 2–3 years. Subsequent research at the Main Botanical Garden in Moscow focused on characterizing Tsitsin’s material, selecting superior germplasm, and expanding genetic diversity through new cycles of hybridization and selection. This work led to the development of a new crop species, Trititrigia, and the release of cultivar ‘Pamyati Lyubimovoy’ in 2020, designed for dual-purpose production of high-quality grain and green biomass. Intermediate wheatgrass (Thinopyrum intermedium) is native to Russia, where several forage cultivars have been released and cultivated. Two large-grain cultivars (Sova and Filin) were developed from populations provided by the Land Institute and are now grown by farmers. Perennial rye was developed through interspecific crosses between Secale cereale and S. montanum, demonstrating persistence for 2–3 years with high biomass production and grain yields of 1.5–2.0 t/ha. Hybridization between Sorghum bicolor and S. halepense resulted in two released cultivars of perennial sorghum used primarily for forage production under arid conditions. Russia’s agroclimatic diversity in agricultural production systems provides significant opportunities for perennial crop development. The broader scientific and practical implications of perennial crops in Russia extend to climate-resilient, sustainable agriculture and international cooperation in this emerging field. Full article

We clarified the effect of wheat straw return combined with microbial agents on rice yield and soil properties. A field experiment was conducted using hybrid indica rice ‘Chuankangyou 2115’ and five treatments: no wheat straw return (T₁), wheat straw return alone (T₂), T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 1:1) (T₃); T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 3:1) (T₄); T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 1:3) (T₅). T₂–T₅ significantly increased dry matter accumulation, soil total N, ammonium N, nitrate N, and organic matter, improving yield by 3.81–26.63%. T₃ exhibited the highest yield increases in two consecutive years. At the jointing and heading stages, Penicillium and Saitozyma dominated under T₃ and positively correlated with dry matter, yield, and nitrogen levels. Straw return combined with Bacillus subtilis and Trichoderma harzianum (20 g m⁻² each) enhanced soil nitrogen availability and dry matter accumulation and translocation. Our findings guide efficient straw utilization, soil microbial regulation, and sustainable high-yield rice production. Full article

This study utilizes waste wheat straw fibers and discarded tea bags as novel raw materials for developing new thermal insulation and sound absorption composites. Wood adhesive (WA) is used to bind the polymer raw materials. Loose polymers and different composites are experimentally developed in different concentrations. Sound absorption and thermal conductivity coefficients are obtained for the developed boards. Bending moment analysis and the moisture content of the boards are reported in addition to a microstructure analysis of the straw fibers from wheat. The results indicate that as the wheat straw fiber’s percentage increases in the composite, the thermal conductivity coefficient decreases, the flexure modulus decreases, the sound absorption coefficient increases at some frequencies, and the moisture content increases. The range of thermal conductivity and the noise reduction coefficient are 0.042–0.073 W/m K and 0.35–0.6 at 24 °C for the polymer raw materials, respectively. The corresponding values for the composites are 0.054 and 0.0575 W/m K and 0.45–0.5, respectively. The maximum moisture content percentages for the polymers and composites are 6.5 and 1.15, respectively. The composite flexure modulus reaches maximum and minimum values of 4.59 MPa and 2.22 MPa, respectively. These promising results promote these polymer and composite sample boards as more convenient insulation materials for green buildings and could replace the conventional petrochemical thermal insulation ones. Full article

To establish a fundamental property database for discrete elements targeting long-fiber materials and address the issue of response surface methodology (RSM) being prone to local optima in high-dimensional nonlinear optimization, this study conducted parameter calibration experiments and validated the calibrated parameters through a combined approach of simulation and physical testing. The Plackett–Burman design and steepest ascent test were employed to screen significant factors. Using the angle of repose (42.3°) obtained from physical experiments as the response value, response surface methodology (RSM) and a particle swarm optimization–back propagation (PSO-BP) neural network model were independently applied to optimize and compare the critical parameters. The results demonstrated that the dynamic friction coefficient between wheat straw particles, the static friction coefficient between wheat straw and steel plate, and the JKR surface energy were the most influential factors on the simulated angle of repose. The PSO-BP model exhibited superior optimization performance compared to RSM, yielding an optimal parameter combination of 0.17, 0.46, and 0.03. The simulated repose angle under these conditions was 41.67°, exhibiting a relative error of only 1.5% compared to the physical experiment. These findings provide a robust theoretical foundation for discrete element simulations of wheat straw feedstock. Full article

Agriculture in India supports millions of livelihoods and is a major force behind economic expansion. Challenges in modern agriculture depend on environmental factors (such as soil quality and climate variability) and biotic factors (such as pests and diseases). These challenges can be addressed by advancements in technology (such as sensors, internet of things, communication, etc.) and data-driven approaches (such as machine learning (ML) and deep learning (DL)), which can help with crop yield and sustainability in agriculture. This study introduces an innovative deep neural network (DNN) approach for identifying leaf diseases in paddy crops at an early stage. The proposed neural network is a hybrid DL model comprising feature extraction, channel attention, inception with residual, and classification blocks. Channel attention and inception with residual help extract comprehensive information about the crops and potential diseases. The classification module uses softmax to obtain the score for different classes. The importance of each block is analyzed via an ablation study. To understand the feature extraction ability of the modules, extracted features at different stages are fed to the SVM classifier to obtain the classification accuracy. This technique was experimented on eight classes with 7857 paddy crop images, which were obtained from local paddy fields and freely available open sources. The classification performance of the proposed technique is evaluated according to accuracy, sensitivity, specificity, F1 score, MCC, area under curve (AUC), and receiver operating characteristic (ROC). The model was fine-tuned by setting the hyperparameters (such as batch size, learning rate, optimizer, epoch, and train and test ratio). Training, validation, and testing accuracies of 99.91%, 99.87%, and 99.49%, respectively, were obtained for 20 epochs with a learning rate of 0.001 and sgdm optimizer. The proposed network robustness was studied via an ablation study and with noisy data. The model’s classification performance was evaluated for other agricultural data (such as mango, maize, and wheat diseases). These research outcomes can empower farmers with smarter agricultural practices and contribute to economic growth. Full article

This study evaluated the effects of fermented banana agro-waste silage (BAWS) in finishing diets for KHAPS pigs (Duroc × MeiShan hybrid). BAWS was produced via 30 days of anaerobic fermentation of disqualified banana fruit, pseudostem, and wheat bran, doubling crude protein content and generating short-chain fatty acids, as indicated by a satisfactory Flieg’s score. Thirty-six pigs were assigned to control (0%), 5%, or 10% BAWS diets formulated to meet NRC nutritional guidelines. Over a 70-day period, BAWS inclusion caused no detrimental effects on growth performance, carcass traits, or meat quality; a transient decline in early-stage weight gain and feed efficiency occurred in the 10% group, while BAWS-fed pigs demonstrated reduced backfat thickness and increased lean area. Fore gut microbiome analysis revealed reduced Lactobacillus and elevated Clostridium sensu stricto 1, Terrisporobacter, Streptococcus, and Prevotella, suggesting enhanced fiber and carbohydrate fermentation capacity. Predictive COG (clusters of orthologous groups)-based functional profiling showed increased abundance of proteins associated with carbohydrate transport (COG2814, COG0561, COG0765) and stress-response regulation (COG2207). These results support BAWS as a sustainable feed ingredient that maintains production performance and promotes fore gut microbial adaptation, with implications for microbiota-informed nutrition and stress resilience in swine. Full article

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), threatens global wheat production. Breeding resistant varieties is a key to disease control. In this study, 198 modern wheat varieties were phenotyped with the prevalent Pst races CYR33 and CYR34 at the seedling stage and with mixed Pst races at the adult-plant stage. Seven stable resistance varieties with infection type (IT) ≤ 2 and disease severity (DS) ≤ 20% were found, including five Chinese accessions (Zhengpinmai8, Zhengmai1860, Zhoumai36, Lantian36, and Chuanmai32), one USA accession (GA081628-13E16), and one Pakistani accession (Pa12). The genotyping applied a 55K wheat single-nucleotide polymorphism (SNP) array. A genome-wide association study (GWAS) identified 14 QTL using a significance threshold of p ≤ 0.001, which distributed on chromosomes 1B (4), 1D (2), 2B (4), 6B, 6D, 7B, and 7D (4 for CYR33, 7 for CYR34, 3 for mixed Pst races), explaining 6.04% to 18.32% of the phenotypic variance. Nine of these QTL were potentially novel, as they did not overlap with the previously reported Yr or QTL loci within a ±5.0 Mb interval (consistent with genome-wide LD decay). The haplotypes and resistance effects were evaluated to identify the favorable haplotype for each QTL. Candidate genes within the QTL regions were inferred based on their transcription levels following the stripe rust inoculation. These resistant varieties, QTL haplotypes, and favorable alleles will aid in wheat breeding for stripe rust resistance. Full article

According to the FAO, wheat, corn, and rice are staple crops that support global food security, providing 50% of the world’s dietary energy. The ability to predict accurately these key food crop agricultural commodity prices is important in stabilizing markets, supporting policymaking, and informing stakeholders’ decisions. To this aim, machine learning (ML), ensemble learning (EL), deep learning (DL), and time series methods (TS) have been increasingly used for forecasting due to the rapid development of computational power and data availability. This study presents a systematic literature review (SLR) of peer-reviewed original research articles focused on forecasting the prices of wheat, corn, and rice using machine learning (ML), deep learning (DL), ensemble learning (EL), and time series techniques. The results of the study help uncover suitable forecasting methods, such as hybrid deep learning models that consistently outperform traditional methods, and they identify important limitations in model interpretability and the use of region-specific datasets, highlighting the need for explainable and generalizable forecasting solutions. This systematic review adheres to the PRISMA 2020 reporting guidelines. Full article

The agriculture sector is a major contributor to the economy of Alberta, Canada, accounting for almost 2.8% of the total GDP. Considering its importance, implementing efficient and cost-effective irrigation systems is vital for promoting sustainable agriculture in semi-arid regions like Lethbridge County, Alberta, Canada. Although irrigation is primarily carried out using the Oldman River and its allied reservoirs, groundwater pumping becomes a supplementary necessity during periods of limited surface water availability or droughts. This research investigates the potential of renewable energy resources, such as wind and solar energy, to meet the energy requirements for crop irrigation. The study begins by identifying and calculating the water requirements for major crops in Lethbridge County, such as wheat and barley, using the United Nations Food and Agriculture Organization’s CROPWAT 8.0 software. Subsequently, energy calculations were conducted to meet the specific crop water demand through the design of a hybrid energy system using Homer Pro 3.16.2. A technoeconomic analysis of the renewable hybrid system has been carried out to demonstrate the efficiency and novelty of the proposed work. Outcomes revealed that the proposed system is both efficient and economical in fulfilling the crop water requirement through groundwater pumping, promoting sustainable agriculture, and helping to ensure food security in the region. Full article

Hybrid necrosis in wheat is caused by an interaction between two genes, Ne1 and Ne2, that triggers the gradual death of plant tissue. This trait affects wheat breeding as the gene Ne2 is the same as the gene Lr13 for leaf rust resistance. We have built a three-marker haplotype that consists of single nucleotide polymorphism (SNP) marker information already available on genotyping arrays for the determination of the presence and absence of Ne2. In this work, test crosses of eight bread wheat varieties with known and unknown Ne1 carriers showed that six of them possessed Ne2. We analyzed a set of wheat varieties which had partial SNPs and phenotypic data, i.e., hybrid necrosis and leaf rust reactions, using Kompetitive Allele-Specific PCR (KASP) markers previously available for Ne2. The observed haplotypes of the SNP markers RAC875_c1226_652, Ra_c4397_542, and AX-110926324 perfectly matched the KASP marker variants for Ne2 and ne2. A prediction, based on these SNP haplotypes, of the distribution of Ne2 in wheat varieties, predominantly from Germany and released between 1900 and 2024, showed that breeding steadily increased the proportion of Ne2 in the German gene pool. Full article

Genomic selection plays a crucial role in breeding programs designed to improve quantitative traits, particularly considering the limitations of traditional methods in terms of accuracy and efficiency. Through the integration of genomic data, breeders are able to obtain more accurate predictions of breeding values. In this study, we proposed and evaluated four deep learning architectures—CNN-LSTM, CNN-ResNet, LSTM-ResNet, and CNN-ResNet-LSTM—that are specifically designed for genomic prediction in crops. After conducting a comprehensive evaluation across multiple datasets, including those for wheat, corn, and rice, the LSTM-ResNet model exhibited superior performance by achieving the highest prediction accuracy in 10 out of 18 traits across four datasets. Additionally, the CNN-ResNet-LSTM model demonstrated notable results, showcasing the best predictive performance for four traits. These findings underscore the efficacy of hybrid models in identifying complex patterns, as they integrate skip connections to mitigate the vanishing gradient problem and enable the extraction of hierarchical features while elucidating intricate relationships among genetic markers. Our analysis of SNP sampling indicated that maintaining SNP counts within the range of 1000 to the full set significantly influences prediction efficiency. Furthermore, we conducted a comprehensive comparative analysis of predictive performance among random selection, marker-assisted selection, and genomic selection utilizing wheat datasets. Collectively, these results provide significant insights into crop genetics, enhancing breeding predictions and advancing global food security and sustainability. Full article

Improving nitrogen use efficiency (NUE) is critical to advancing sustainable cereal production, particularly under Mediterranean conditions where environmental pressures challenge input-intensive practises. This study evaluates NUE in Tritordeum, a climate-resilient wheat–barley hybrid, using a holistic experimental approach that integrates pre- and post-harvest soil analyses, including an electrical conductivity (EC) assessment, plant and seed nutrient profiling, and an evaluation of yield performance and nitrogen ratio dynamics. Four treatments were tested: conventional urea (T1), urea with an urease inhibitor (NBPT) (T2), urea with a nitrification inhibitor (DCD) (T3), and an unfertilised control (C). While conventional urea achieved the highest yield (1366 kg ha⁻¹), enhanced-efficiency fertilisers (EEFs) improved nutrient synchronisation and seed nutritional quality. Specifically, EEFs increased seed zinc (T2: 34.93 mg/kg), iron (T1: 33.77 mg/kg), and plant potassium (T2: 1.66%; T3: 1.61%) content, and also improved nitrogen remobilisation (elevated Nplant/Nseed ratios). EEFs also influenced soil properties, increasing organic matter (T3: 2.75%) and EC (T3: 290.78 μS/cm). These findings suggest that while EEFs may not always boost yield in the short term, they contribute to long-term soil fertility and nutrient density in grain. This study underscores the importance of synchronising nitrogen availability with Tritordeum’s phenological stages and highlights the crop’s suitability for sustainable, low-input agriculture under climate variability. Full article