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To sustainably increase wheat yield to meet the growing world population's food demand in the face of climate change, Conservation Agriculture (CA) is a promising approach. Still, there is a lack of genomic studies investigating the genetic basis of crop adaptation to CA. To dissect the genetic architecture of 19 morpho-physiological traits that could be involved in the enhanced adaptation and performance of genotypes under CA, we performed GWAS to identify MTAs under four contrasting production regimes viz., conventional tillage timely sown (CTTS), conservation agriculture timely sown (CATS), conventional tillage late sown (CTLS) and conservation agriculture late sown (CALS) using an association panel of 183 advanced wheat breeding lines along with 5 checks. Traits like Phi2 (Quantum yield of photosystem II; CATS:0.37, CALS: 0.31), RC (Relative chlorophyll content; CATS:55.51, CALS: 54.47) and PS1 (Active photosystem I centers; CATS:2.45, CALS: 2.23) have higher mean values in CA compared to CT under both sowing times. GWAS identified 80 MTAs for the studied traits across four production environments. The phenotypic variation explained (PVE) by these QTNs ranged from 2.15 to 40.22%. Gene annotation provided highly informative SNPs associated with Phi2, NPQ (Quantum yield of non-photochemical quenching), PS1, and RC which were linked with genes that play crucial roles in the physiological adaptation under both CA and CT. A highly significant SNP AX94651261 (9.43% PVE) was identified to be associated with Phi2, while two SNP markers AX94730536 (30.90% PVE) and AX94683305 (16.99% PVE) were associated with NPQ. Identified QTNs upon validation can be used in marker-assisted breeding programs to develop CA adaptive genotypes., (© 2024. The Author(s).)

Straw return is an effective measure to promote sustainable agriculture by significantly improving soil fertility. At present, few studies have been conducted on the most effective carbon enhancing management measures for various crops. Therefore, we conducted a meta-analysis using data collected from 184 literature sources, comprising 3297 data sets to analyze the carbon increase effects of straw returning in three main crops (rice, maize, and wheat) in China and to explore the influence mechanism of natural factors, soil properties, straw return measures, and cropping systems on the carbon enhancement effect. The study showed that straw return significantly increased soil organic carbon and the rate of increase was higher for wheat at 15.88% (14.74%-17.03%) than for rice at 12.7% (11.5%-13.91%) and maize at 12.42% (11.42%-13.42%), with varying degrees of improvement in other soil physicochemical properties. Natural factors have the greatest impact on the carbon increasing effect of rice fields, reaching 28.8%, especially at temperature between 10 °C and 15 °C, less than 800 mm precipitation, low latitude, and short frost-free period. Maize and wheat are most affected by soil properties, reaching 41% and 34.5% respectively. Furthermore, field management practices also play a pivotal role, organic carbon increasing obviously was observed when the C/N ratio of exogenous nutrients is bigger than 20 with the low initial organic matter. Shallow tillage and less than 7.5 t hm -2 straw returning with 3-10 years to the field are ideal for rice and maize. Crop rotation, especially in drylands, increased soil organic carbon more significantly than continuous. The results of our analysis can provide valuable insights into the effect of straw return on carbon increase. In the future, the soil carbon can be improved by adopting rational cropping patterns and straw return measures with taking into account climate and soil characteristics for different crops., Competing Interests: Declaration of competing interest There is no conflict of interest between the authors., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The complex evolutionary history of wheat has shaped its associated root microbial community. However, consideration of impacts from agricultural intensification has been limited. This study investigated how endogenous (genome polyploidization) and exogenous (introduction of chemical fertilizers) factors have shaped beneficial rhizobacterial selection. We combined culture-independent and -dependent methods to analyze rhizobacterial community composition and its associated functions at the root-soil interface from a range of ancestral and modern wheat genotypes, grown with and without the addition of chemical fertilizer. In controlled pot experiments, fertilization and soil compartment (rhizosphere, rhizoplane) were the dominant factors shaping rhizobacterial community composition, whereas the expansion of the wheat genome from diploid to allopolyploid caused the next greatest variation. Rhizoplane-derived culturable bacterial collections tested for plant growth-promoting (PGP) traits revealed that fertilization reduced the abundance of putative plant growth-promoting rhizobacteria in allopolyploid wheats but not in wild wheat progenitors. Taxonomic classification of these isolates showed that these differences were largely driven by reduced selection of beneficial root bacteria representative of the Bacteroidota phylum in allopolyploid wheats. Furthermore, the complexity of supported beneficial bacterial populations in hexaploid wheats was greatly reduced in comparison to diploid wild wheats. We therefore propose that the selection of root-associated bacterial genera with PGP functions may be impaired by crop domestication in a fertilizer-dependent manner, a potentially crucial finding to direct future plant breeding programs to improve crop production systems in a changing environment., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Society for Microbial Ecology.)

Agriculture is one of the most important sectors in the Indian context. It is one of the highest employing sectors in the Indian scenario. Unlike other sectors agriculture is highly dependent on the quality and the quantity of both the external factors like rainfall, climate, pH of the soil, fertilizers and insecticides used, and internal factors like the quality of seeds. This paper predicts the production of crops as a function of rainfall for four Indian States. This knowledge can be implemented in generating a rough overview of how the production is based on rainfall and how much can a specific crop production for the amount of rainfall it receives. Two crops each belonging to four different states are chosen and the best regression model for the crop of the state is chosen. There is no research done solely on how rainfall affects crops of particular states. The proposed method of evaluation is better than other existing methods of evaluation as it evaluates all the regression techniques (Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest, and XGBRegression) for two crops of four individual states. For balanced evaluation, two states of North India and two states of South India are selected. The regression techniques are evaluated based on their Mean Squared Error., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Active nitrifiers and rapid nitrification are major contributing factors to nitrogen losses in global wheat production. Suppressing nitrifier activity is an effective strategy to limit N losses from agriculture. Production and release of nitrification inhibitors from plant roots is termed "biological nitrification inhibition" (BNI). Here, we report the discovery of a chromosome region that controls BNI production in "wheat grass" Leymus racemosus (Lam.) Tzvelev, located on the short arm of the "Lr#3Ns b " (Lr#n), which can be transferred to wheat as T3BL.3Ns b S (denoted Lr#n-SA), where 3BS arm of chromosome 3B of wheat was replaced by 3Ns b S of L. racemosus We successfully introduced T3BL.3Ns b S into the wheat cultivar "Chinese Spring" (CS-Lr#n-SA, referred to as "BNI-CS"), which resulted in the doubling of its BNI capacity. T3BL.3Ns b S from BNI-CS was then transferred to several elite high-yielding hexaploid wheat cultivars, leading to near doubling of BNI production in "BNI-MUNAL" and "BNI-ROELFS." Laboratory incubation studies with root-zone soil from field-grown BNI-MUNAL confirmed BNI trait expression, evident from suppression of soil nitrifier activity, reduced nitrification potential, and N 2 O emissions. Changes in N metabolism included reductions in both leaf nitrate, nitrate reductase activity, and enhanced glutamine synthetase activity, indicating a shift toward ammonium nutrition. Nitrogen uptake from soil organic matter mineralization improved under low N conditions. Biomass production, grain yields, and N uptake were significantly higher in BNI-MUNAL across N treatments. Grain protein levels and breadmaking attributes were not negatively impacted. Wide use of BNI functions in wheat breeding may combat nitrification in high N input-intensive farming but also can improve adaptation to low N input marginal areas., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Plant-beneficial Pseudomonas bacteria hold the potential to be used as inoculants in agriculture to promote plant growth and health through various mechanisms. The discovery of new strains tailored to specific agricultural needs remains an open area of research. In this study, we report the isolation and characterization of four novel Pseudomonas species associated with the wheat rhizosphere. Comparative genomic analysis with all available Pseudomonas type strains revealed species-level differences, substantiated by both digital DNADNA hybridization and average nucleotide identity, underscoring their status as novel species. This was further validated by the phenotypic differences observed when compared to their closest relatives. Three of the novel species belong to the P. fluorescens species complex, with two representing a novel lineage in the Pseudomonas phylogeny. Functional genome annotation revealed the presence of specific features contributing to rhizosphere colonization, including flagella and components for biofilm formation. The novel species have the genetic potential to solubilize nutrients by acidifying the environment, releasing alkaline phosphatases and their metabolism of nitrogen species, indicating potential as biofertilizers. Additionally, the novel species possess traits that may facilitate direct promotion of plant growth through the modulation of the plant hormone balance, including the ACC deaminase enzyme and auxin metabolism. The presence of biosynthetic clusters for toxins such as hydrogen cyanide and nonribosomal peptides suggests their ability to compete with other microorganisms, including plant pathogens. Direct inoculation of wheat roots significantly enhanced plant growth, with two strains doubling shoot biomass. Three of the strains effectively antagonized fungal phytopathogens (Thielaviopsis basicola, Fusarium oxysporum, and Botrytis cinerea), demonstrating their potential as biocontrol agents. Based on the observed genetic and phenotypic differences from closely related species, we propose the following names for the four novel species: Pseudomonas grandcourensis sp. nov., type strain DGS24T (= DSM 117501T = CECT 31011T), Pseudomonas purpurea sp. nov., type strain DGS26T (= DSM 117502T = CECT 31012T), Pseudomonas helvetica sp. nov., type strain DGS28T (= DSM 117503T = CECT 31013T) and Pseudomonas aestiva sp. nov., type strain DGS32T (= DSM 117504T = CECT 31014T). [ABSTRACT FROM AUTHOR]

To effectively reveal the disaster-causing mechanism between water stress and yield loss under different drought combinations during multiple growth periods of winter wheat, based on biennial wheat drought experiments, a crop growth analysis method was used to quantitatively identify and assess wheat yield loss sensitivity. The results showed that there was a significant negative correlation between the total dry matter relative growth rate (RGR) of wheat and the daily average degree of drought stress. The average determination coefficients of logarithmic fitting for 2017 and 2018 were 0.7935 and 0.7683, respectively. Wheat dry matter accumulation differed under the different drought combination scenarios. The yield loss sensitivity response relationship between the decrease in the RGR of wheat dry matter (relative to no drought stress) and the daily average degree of drought stress could be quantitatively identified by an S-shaped curve, and the 2017 and 2018 average coefficients of determination R2 were 0.859 and 0.849, respectively. Mild drought stress at the tillering stage stimulates adaptability and has little effect on yield. The soil water content (SWC) can be controlled to 65–75% of the field water holding capacity; the SWC at the jointing and booting stage can be controlled to be higher than the field water holding capacity of 55%. The SWC was maintained at a level higher than 75% of the field water holding capacity during the heading and flowering stages and the grain-filling and milky stages to achieve a harmonization of yields and water savings. In addition, during the production process, continuous severe drought during the jointing and booting stage and the heading and flowering stage should be avoided. This study elucidates the response relationship between drought intensity and drought-induced losses from the perspective of physical genesis, provides effective irrigation guidance for regional wheat planting, lays the foundation for the construction of quantitative agricultural drought loss risk curves, and provides technical support for predicting the trend of yield losses in wheat under different drought stresses. [ABSTRACT FROM AUTHOR]

Wheat sharp eyespot is a soil-borne fungal disease commonly found in wheat areas in China, which can occur throughout the entire reproductive period of wheat and has a great impact on the yield and quality of wheat in China. By constructing a domain ontology for wheat sharp eyespot control and modeling the domain knowledge, we aim to integrate and share the knowledge in the field of wheat sharp eyespot control, which can provide important support and guidance for agricultural decision-making and disease control. In this study, the literature in the field of wheat sharp eyespot control was used as a data source, the KeyBERT keyword extraction algorithm was used to mine the core concepts of the ontology, and the hierarchical relationships among the ontology concepts were extracted through clustering. Based on the constructed ontology of wheat sharp eyespot control, the schema of knowledge extraction was formed, and the knowledge extraction model was trained using the ERNIE 3.0 knowledge enhancement pretraining model. This study proposes a model and algorithm to realize knowledge extraction based on domain ontology, describes the construction method and process framework of wheat sharp eyespot control domain ontology, and details the training and reasoning effect of the knowledge extraction model. The knowledge extraction model constructed in this study for wheat sharp eyespot control contains a more complete conceptual system of wheat sharp eyespot. The F1 value of the model reaches 91.26%, which is a 17.86% improvement compared with the baseline model, and it can satisfy the knowledge extraction needs in the field of wheat sharp eyespot control. This study can provide a reference for domain knowledge extraction and provide strong support for knowledge discovery and downstream applications such as intelligent Q&A and intelligent recommendation in the field of wheat sharp eyespot control. [ABSTRACT FROM AUTHOR]

Existing agricultural nitrogen datasets in China are mostly developed using coarse national or provincial statistics. A crop‐specific nitrogen rate dataset based on the finest‐scale agricultural nitrogen statistics at the county level remains lacking. Here, we constructed a new dataset (CN‐N), which provides annual nitrogen rates for rice, wheat and maize in China at a 1‐km spatial resolution from 2004 to 2016. This dataset was developed by harmonizing county‐level and provincial agricultural nitrogen statistics with gridded crop distribution maps, resulting in 13 years of nitrogen rate maps for each crop covering 2004–2016. Validation against farmers' surveys by crop indicates CN‐N reliably quantifies average nitrogen rates and trends for each crop over 2004–2016, demonstrating improved spatial heterogeneity compared to previous datasets rasterized using only provincial statistics. Our study provides a crop‐specific, temporally consistent, gridded nitrogen rate dataset based on the finest‐scale county‐level agricultural nitrogen statistics. This can support future process‐based modelling for sustainable agricultural nitrogen management strategies. [ABSTRACT FROM AUTHOR]

This article discusses the role of the transcription factor TaMYB72 in promoting heading and enhancing grain yield traits in wheat. The researchers used CRISPR/Cas9-based gene editing to create knockout mutants of TaMYB72 and found that these mutants displayed later heading dates and increased grain yield traits compared to the wild-type plants. The study also revealed that TaMYB72 directly activates the expression of TaFT, a gene involved in flowering and spikelet development, to regulate the height and yield traits of wheat. These findings provide valuable insights into the genetic basis of grain yield traits and heading date in wheat, and suggest TaMYB72 as a potential target gene for increasing grain yield in breeding programs. [Extracted from the article]

Prebiotics, a subset of biostimulants, have garnered attention for their potential to enhance soil conditions and promote plant growth, offering a promising alternative to conventional agricultural inputs. This study explores how two commercial prebiotics, K1® and NUTRIGEO L® (SPK and SPN), impact soil functions compared to a control (SP). The experiment involved agricultural soil amended with organic wheat straws and cultivated with Zea mays L. Previous research demonstrated substantial effects of these prebiotics on plant biomass, soil parameters, and microbial community ten weeks after application. The present study delves deeper, focusing on soil microbial abundance, enzyme activities, and metabolic diversity. Analysis revealed that SPN notably increased the fungi-to-bacteria ratio, and both prebiotics elevated the activity of several key enzymes. SPN enhanced α-glucosidase and β-galactosidase activities, while SPK increased arylsulfatase, phosphatase, alkaline phosphatase, and urease activities. Enzymatic indexes confirmed the positive impact on soil functional diversity and fertility. Additionally, prebiotic treatments showed distinct metabolic profiles, with SPK degrading eleven carbon sources more rapidly across five groups and SPN accelerating the decomposition rate of four carbon sources from three groups. These findings highlight the ability of prebiotics to shape microbial communities and enhance soil fertility by modulating their functional activity and diversity. [ABSTRACT FROM AUTHOR]

The number of wheat spikes has an important influence on wheat yield, and the rapid and accurate detection of wheat spike numbers is of great significance for wheat yield estimation and food security. Computer vision and machine learning have been widely studied as potential alternatives to human detection. However, models with high accuracy are computationally intensive and time consuming, and lightweight models tend to have lower precision. To address these concerns, YOLO-FastestV2 was selected as the base model for the comprehensive study and analysis of wheat sheaf detection. In this study, we constructed a wheat target detection dataset comprising 11,451 images and 496,974 bounding boxes. The dataset for this study was constructed based on the Global Wheat Detection Dataset and the Wheat Sheaf Detection Dataset, which was published by PP Flying Paddle. We selected three attention mechanisms, Large Separable Kernel Attention (LSKA), Efficient Channel Attention (ECA), and Efficient Multi-Scale Attention (EMA), to enhance the feature extraction capability of the backbone network and improve the accuracy of the underlying model. First, the attention mechanism was added after the base and output phases of the backbone network. Second, the attention mechanism that further improved the model accuracy after the base and output phases was selected to construct the model with a two-phase added attention mechanism. On the other hand, we constructed SimLightFPN to improve the model accuracy by introducing SimConv to improve the LightFPN module. The results of the study showed that the YOLO-FastestV2-SimLightFPN-ECA-EMA hybrid model, which incorporates the ECA attention mechanism in the base stage and introduces the EMA attention mechanism and the combination of SimLightFPN modules in the output stage, has the best overall performance. The accuracy of the model was P=83.91%, R=78.35%, AP= 81.52%, and F1 = 81.03%, and it ranked first in the GPI (0.84) in the overall evaluation. The research examines the deployment of wheat ear detection and counting models on devices with constrained resources, delivering novel solutions for the evolution of agricultural automation and precision agriculture. [ABSTRACT FROM AUTHOR]

The goal of public breeding programs is to develop and disseminate improved varieties to farmers. This strategy aims at providing farming communities with superior crop varieties than they are growing. However, the strategy rarely considers the needs and preferences of farmers, especially gendered preferences, failing to solve real field problems by addressing the differences and inequalities prevalent in the farming communities. Our research examines how personal, household, agronomic and ecological characteristics of wheat growers in Bihar, India's eastern Indo-Gangetic Plains, affect women and men's wheat trait choices. Data were obtained from 1,003 households where both male and female respondents from the same household were interviewed. We accounted for 23 traits of wheat from a careful assessment of production, environment, cooking quality, market demand, and esthetic criteria. Binomial logistic regression was used to determine women's and men's trait preferences. The results imply that gender influences the preferences of wheat traits. Some traits are favored by both women and men, however, in other instances, there are striking disparities. For example, men choose wheat varieties that are well adapted to extreme climate conditions, have a higher grain yield, and produce chapati with a superior taste, while women prefer wheat types with superior chapati making quality, higher grain yield, and high market prices. Other socioeconomic, agronomic, cultural, and geolocational factors have a considerable impact on trait preferences. These human dimensions of traits preferred by women and men farmers are important for trait combinations to develop breeding product profiles for certain market segments. [ABSTRACT FROM AUTHOR]

In crops like wheat, terminal drought is one of the principal stress factors limiting productivity in rain-fed systems. However, little is known about root development after heading, when water uptake can be critical to wheat crops. The impact of water-stress on root growth was investigated in two wheat cultivars, Scout and Mace, under well-watered and post-anthesis water stress in three experiments. Plants were grown outside in 1.5-m long pots at a density similar to local recommended farming practice. Differences in root development were observed between genotypes, especially for water stress conditions under which Scout developed and maintained a larger root system than Mace. While under well-watered conditions both genotypes had shallow roots that appeared to senesce after heading, a moderate water stress stimulated shallow-root growth in Scout but accelerated senescence in Mace. For deep roots, post-heading biomass growth was observed for both genotypes in well-watered conditions, while under moderate water stress, only Scout maintained net growth as Mace deep roots senesced. Water stress of severe intensity affected both genotypes similarly, with root senescence at all depths. Senescence was also observed above ground. Under well-watered conditions, Scout retained leaf greenness (i.e. stay-green phenotype) for slightly longer than Mace. The difference between genotypes accentuated under moderate water stress, with rapid post-anthesis leaf senescence in Mace while Scout leaf greenness was affected little if at all by the stress. As an overall result, grain biomass per plant (‘yield’) was similar in the two genotypes under well-watered conditions, but more affected by a moderate stress in Mace than Scout. The findings from this study will assist improvement in modelling root systems of crop models, development of relevant phenotyping methods and selection of cultivars with better adaptation to drought. [ABSTRACT FROM AUTHOR]

Climate change is one of the main challenges, and it poses a tough challenge to the agriculture industry globally. Additionally, greenhouse gas (GHG) emissions are the main contributor to climate change; however, croplands are a prominent source of GHG emissions. Yet this complex challenge can be mitigated through climate-smart agricultural practices. Conservation tillage is commonly known to preserve soil and mitigate environmental change by reducing GHG emissions. Nonetheless, there is still a paucity of information on the influences of conservation tillage on wheat yield, soil properties, and GHG flux, particularly in the semi-arid Dingxi belt. Hence, in order to fill this gap, different tillage systems, namely conventional tillage (CT) control, straw incorporation with conventional tillage (CTS), no-tillage (NT), and stubble return with no-tillage (NTS), were laid at Dingxi, Gansu province of China, under a randomized complete block design with three replications to examine their impacts on yield, soil properties, and GHG fluxes. Results depicted that different conservative tillage systems (CTS, NTS, and NT) significantly (p < 0.05) increased the plant height, number of spikes per plant, seed number per meter square, root yield, aboveground biomass yield, thousand-grain weight, grain yield, and dry matter yield compared with CT. Moreover, these conservation tillage systems notably improved the soil properties (soil gravimetric water content, water-filled pore space, water storage, porosity, aggregates, saturated hydraulic conductivity, organic carbon, light fraction organic carbon, carbon storage, microbial biomass carbon, total nitrogen, available nitrogen storage, microbial biomass nitrogen, total phosphorous, available phosphorous, total potassium, available potassium, microbial counts, urease, alkaline phosphatase, invertase, cellulase, and catalase) while decreasing the soil temperature and bulk density over CT. However, CTS, NTS, and NT had non-significant effects on ECe, pH, and stoichiometric properties (C:N ratio, C:P ratio, and N:P ratio). Additionally, conservation-based tillage regimes NTS, NT, and CTS significantly (p < 0.05) reduced the emission and net global warming potential of greenhouse gases (carbon dioxide, methane, and nitrous oxide) by 23.44, 19.57, and 16.54%, respectively, and decreased the greenhouse gas intensity by 23.20, 29.96, and 18.72%, respectively, over CT. We conclude that NTS is the best approach to increasing yield, soil and water conservation, resilience, and mitigation of agroecosystem capacity. [ABSTRACT FROM AUTHOR]

Precision agriculture involves the application of advanced technologies to improve agricultural productivity, efficiency, and profitability while minimizing waste and environmental impacts. Deep learning approaches enable automated decision-making for many visual tasks. However, in the agricultural domain, variability in growth stages and environmental conditions, such as weather and lighting, presents significant challenges to developing deep-learning-based techniques that generalize across different conditions. The resource-intensive nature of creating extensive annotated datasets that capture these variabilities further hinders the widespread adoption of these approaches. To tackle these issues, we introduce a semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process, requiring minimal manual data annotation. Using only three manually annotated images and a selection of video clips from wheat fields, we generated a large-scale computationally annotated dataset of image–mask pairs and a large dataset of unannotated images extracted from video frames. We developed a two-branch convolutional encoder–decoder model architecture that uses both synthesized image–mask pairs and unannotated images, enabling effective adaptation to real images. The proposed model achieved a Dice score of 80.7% on an internal test dataset and a Dice score of 64.8% on an external test set composed of images from five countries and spanning 18 domains, indicating its potential to develop generalizable solutions that could encourage the wider adoption of advanced technologies in agriculture. [ABSTRACT FROM AUTHOR]

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates. [ABSTRACT FROM AUTHOR]

Food security is a crucial requirement in today's world to meet the dietary needs of individuals. As the population continues to grow, the demand for food will increase by 70 to 100 percent by 2050. Therefore, there is an urgent need to develop an approach that can assist farmers in predicting crop yield accurately and in a timely manner before crop harvesting. In this direction, the present study proposed a nature-inspired optimized hybrid convolutional neural network with bidirectional long short-term memory to extract the nonlinear complex relationships among crop attributes. This hybrid deep learning model was optimized using a particle swarm optimization approach to automate the selection of appropriate hyperparameters for wheat yield prediction. The proposed model was developed to estimate wheat yield in a major wheat-producing state in India from 2000 to 2018 using the temporal and spatial characteristics of wheat crops. The experiment was conducted by integrating the historical yield, meteorological data, remote sensing-derived indices, and soil parameters from the October to April season. Furthermore, we also evaluated the performance of the proposed model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE) with publicly available datasets such as Agro_data, Soybean_data, and FAO_data. The experimental results showed that the proposed PSO-CNN-Bi-LSTM method achieved an MAE of 0.39, MSE of 0.18, and RMSE of 0.42 (tonnes/ha) and outperformed existing CNN, LSTM, CNN-LSTM, CNN-Bi-LSTM, CNN-LSTM-PSO, CNN-Bi-LSTM-BO-GO, and neural network methods. Our findings demonstrated that the proposed methodology could be a promising alternative for predicting crop yield. [ABSTRACT FROM AUTHOR]

Wheat spike detection is crucial for estimating wheat yields and has a significant impact on the modernization of wheat cultivation and the advancement of precision agriculture. This study explores the application of the DETR (Detection Transformer) architecture in wheat spike detection, introducing a new perspective to this task. We propose a high-precision end-to-end network named WH-DETR, which is based on an enhanced RT-DETR architecture. Initially, we employ data augmentation techniques such as image rotation, scaling, and random occlusion on the GWHD2021 dataset to improve the model's generalization across various scenarios. A lightweight feature pyramid, GS-BiFPN, is implemented in the network's neck section to effectively extract the multi-scale features of wheat spikes in complex environments, such as those with occlusions, overlaps, and extreme lighting conditions. Additionally, the introduction of GSConv enhances the network precision while reducing the computational costs, thereby controlling the detection speed. Furthermore, the EIoU metric is integrated into the loss function, refined to better focus on partially occluded or overlapping spikes. The testing results on the dataset demonstrate that this method achieves an Average Precision (AP) of 95.7%, surpassing current state-of-the-art object detection methods in both precision and speed. These findings confirm that our approach more closely meets the practical requirements for wheat spike detection compared to existing methods. [ABSTRACT FROM AUTHOR]

Salinity is a significant environmental factor that prevents plant germination and growth, thus affecting agricultural productivity globally. Plant growth regulators, such as 6-benzylaminopurine, gibberellin, and melatonin, have been studied for their ability to mitigate the adverse effects of salt stress on wheat, a salt-tolerant staple crop cultivated worldwide. A field experiment was conducted before planting, involving three wheat varieties (Al-Ezz, Dijlah, and Al-Rasheed) subjected to different seed treatment combinations (salt tempering as control, salt tempering + benzyl stimulation, salt tempering + gibberellin stimulation, and salt tempering + melatonin stimulation), all irrigated with saline well water (6 ds m-2) except for the control group irrigated with river water. The findings highlighted the superior performance of the Al-Rasheed variety, exhibiting the highest levels of chlorophyll, relative water content, peroxidase enzyme, and catalase activity (1.993 mg g-1, 83.51%, 40.09 units ml-1, 34.54 units ml-1, respectively). The combination of salt-adaptation treatment and benzyl stimulation resulted in the highest chlorophyll content (2.117 mg g-1), while salt-adaptation treatment and melatonin stimulation showed the highest peroxidase and catalase activity (47.46 units ml-1, 42.71 units ml-1), along with increased grain yield (58.34 g) and harvest index (48.43%). Melatonin and benzylaminopurine have demonstrated potential in alleviating salt stress in plants by influencing various physiological traits.

A field experiment on loamy soil was conducted in the Haditha district, Anbar province, Iraq, during the winter season of 2021-2022. The study aimed to determine the effect of different zinc levels and methods on wheat growth, yield, and tolerance to salinity. The experiment used a randomized complete block design (RCBD) with a split-plot arrangement and three replications. The seeds were soaked before planting with three zinc levels: 0 (control treatment, soaked in water only), 75 mg Zn Kg-1 (seeds soaked in zinc at a concentration of 75 mg Zn Kg-1), and 150 mg Zn Kg-1 (seeds soaked in zinc at a concentration of 150 mg Zn Kg-1) for eight hours. This was followed by two spray applications of the same concentrations, the first at the tillering stage and the second at the booting stage. Statistical analysis showed that adding zinc to soak the seeds and spraying it on the plants increased plant height, number of tillers, flag leaf area, grain yield, and 1000-grain weight. Additionally, it was observed that seed soaking and plant spraying with zinc increased the plant's tolerance to salinity by reducing the chloride and sodium concentration in the wheat straw and roots and increasing the potassium concentration and the potassium/sodium ratio in the wheat straw and roots. The best results were obtained using the 150 mg Zn Kg-1 level (seed soaking and plant spraying) for most of the studied characteristics.

A laboratory experiment was carried out under the Seed Technology Laboratory conditions, Field Crops Department, College of Agriculture, Anbar University, Iraq, during the 2021 season to study the effect of coating with yeast extract at different concentrations (0, 15, 20, and 25 g L-1) on germination, seedling characteristics, and enzymatic activity under heat stress (5, 10, 15, and 20 °C) of wheat seeds of the Ibaa-99 cultivar. The experiment was conducted using a factorial design in a completely randomized design (CRD) with four repetitions. The obtained results showed a significant response of wheat seeds to the yeast extract treatments, with the 25 g L-1 yeast extract coating treatment showing superiority in germination speed (59.75%), final germination percentage (69.25%), radicle and plumule lengths (9.453 cm, 8.429 cm), seedling vigor index (1662.5), and enzyme activity (10.394 for catalase and 53.46 for peroxidase). Regarding cultivation under heat stress conditions, the temperature of 20 °C was significantly better for germination speed (67.25%), final germination percentage (87.62%), radicle and plumule lengths (10.703 cm and 9.551 cm), seedling vigor index (1812.3), and enzyme activity (catalase and peroxidase: 13.474 and 56.2). The interaction between coating seeds with 25 g yeast extract L-1 and growing at 20 °C achieved the highest values for germination speed, final germination percentage, radicle and plumule lengths, and seedling vigor index, while the same coating treatment at 10 °C recorded the highest activity of catalase and peroxidase enzymes.

A field experiment was carried out during the winter season of 2021-2022 in the Nuaimiya area of Fallujah City, Anbar Governorate, to study the effect of three potassium levels on the growth and yield of the wheat crop. The experiment was conducted using a randomized complete block design (RCBD) according to the arrangement of the split-plot with three replicates. The main plots included three varieties of wheat (Ibaa 99, Al-Rasheed, and Abu Ghraib-3), while the sub-plots included three levels of potassium: 0, 100, and 200 kg K ha-1. The results showed the superiority of the Al-Rasheed variety in each of the following: flag leaf area (46.13 cm²), spike length (14.8 cm), number of spikes per square meter (416.1 spikes m-2), number of grains per spike (52.33 grains spike-1), and total grain yield (4.963 tons ha-1). In contrast, the Ibaa 99 variety gave the highest average plant height of 121.11 cm. The Abu Ghraib-3 variety recorded the highest average weight of 1000 grains at 47.8 g. Potassium fertilizer levels significantly affected most growth and yield traits. The 200 kg K ha-1 level gave the highest means for plant height (119.63 cm), flag leaf area (41.51 cm²), spike length (13.65 cm), number of spikes per square meter (417.0 spikes m-2), number of grains per spike (55.22 grains spike-1), and total grain yield (5.004 tons ha-1). There was a significant interaction between the Ibaa 99 variety and the 200 kg K ha-1 level of potassium fertilizer in plant height, and between the Al-Rasheed variety and the same level in flag leaf area, spike length, number of spikes per square meter, and number of grains per spike.

Mature wheat kernels contain three main parts: endosperm, bran, and germ. Flour milling results in multiple streams that are chemically different; however, the distribution of antioxidants and phenolic compounds has not been well documented in terms of conventional milling by-product streams. In this study, multiple analytical methods were used to investigate antioxidant activity and phenolic compound compositions of hard red winter wheat (whole ground wheat), the parts of a wheat kernel (bran, flour, germ), and wheat by-product streams (mill feed, red dog, shorts) for the first time. For each mill stream, phenolic compounds (total, flavonoid, and anthocyanin contents) were determined and antioxidant activities were evaluated with 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical-scavenging activity, ferric reducing/antioxidant power (FRAP), and total antioxidant capacity assays. Significant differences (P

Genome-wide association mapping studies (GWAS) based on Big Data are a potential approach to improve marker-assisted selection in plant breeding. The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing. Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects, but is hindered by data heterogeneity and lack of interoperability. In this study, we used genomic and phenotypic data sets, focusing on Central European winter wheat populations evaluated for heading date. We explored strategies for integrating these data and subsequently the resulting potential for GWAS. Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols, resulting in high quality integrated phenotypic data. In this context, genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series, which was low in our case. Contrary to expectations, fewer associations between markers and traits were found in the larger combined data than in the individual experimental series. However, the predictive power based on the marker-trait associations of the integrated data set was higher across data sets. Therefore, the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS. The results encourage further efforts to standardize and share data in the plant breeding community.

Lesion mimic often exhibits leaf disease-like symptoms even in the absence of pathogen infection, and is characterized by a hypersensitive-response (HR) that closely linked to plant disease resistance. Despite this, only a few lesion mimic genes have been identified in wheat. In this investigation, a lesion mimic wheat mutant named je0297 was discovered, showing no alteration in yield components when compared to the wild type (WT). Segregation ratio analysis of the F2 individuals resulting from the cross between the WT and the mutant revealed that the lesion mimic was governed by a single recessive gene in je0297. Using Bulked segregant analysis (BSA) and exome capture sequencing, we mapped the lesion mimic gene designated as lm6 to chromosome 6BL. Further gene fine mapping using 3315 F2 individuals delimited the lm6 within a 1.18 Mb region. Within this region, we identified 16 high-confidence genes, with only two displaying mutations in je0297. Notably, one of the two genes, responsible for encoding flavonol synthase, exhibited altered expression levels. Subsequent phenotype analysis of TILLING mutants confirmed that the gene encoding flavonol synthase was indeed the causal gene for lm6. Transcriptome sequencing analysis revealed that the DEGs between the WT and mutant were significantly enriched in KEGG pathways related to flavonoid biosynthesis, including flavone and flavonol biosynthesis, isoflavonoid biosynthesis, and flavonoid biosynthesis pathways. Furthermore, more than 30 pathogen infection-related (PR) genes exhibited upregulation in the mutant. Corresponding to this expression pattern, the flavonoid content in je0297 showed a significant decrease in the 4th leaf, accompanied by a notable accumulation of reactive oxygen, which likely contributed to the development of lesion mimic in the mutant. This investigation enhances our comprehension of cell death signaling pathways and provides a valuable gene resource for the breeding of disease-resistant wheat.

In a study comparing grain filling and yield in a large- and a small-grain-size wheat cultivar under two planting patterns and two irrigation regimes, plastic-covered ridge and furrow planting with sprinkler irrigation increased grain filling and yield in both cultivars. The largest contributors to grain yield were an extended active grain-filling period in Shuangda 1 and an increased mean grain-filling rate in XN538.

A factorial experiment was carried out by foliar application of zinc in two forms zinc sulphate (ZnSO4) and zinc oxide nanoparticles (ZnO NPs) prepared from aqueous zinc acetate using a wet chemistry method, which were characterized using an atomic force microscope (AFM) as having dimensions of 40-80 nanometres. The foliar of two zinc forms were applied in three concentrations (25, 50 and 100 ppm) in addition to distilled water with the aim of studying their effect on the growth and productivity of durum wheat plants ( Triticum durum, L var. Sham 7) grown under the conditions of Deir Ez-Zor Governorate-Syria. The results showed that adding zinc had a significant effect on all tested morphological indicators (plant heights - chlorophyll - number of branches/plant - number of spikes /plant) and productivity (1000 kernel weight, grain and straw productivity), as well as the grain content of some elements (nitrogen, phosphorus, potassium and zinc). The treatments for adding zinc oxide nanoparticles were significantly superior to those of zinc sulphate in all tested indicators. as the lowest values for the tested indicators were recorded in the regular zinc sulphate treatment at a concentration of 25 ppm compared to the rest of the treatments except for the control treatment, while the Nano zinc oxide treatments at a concentration of 50 and 100 ppm showed superiority significantly in all studied indicators especially in increasing wheat productivity by 24- 26%, compared to the control treatment.

Laboratory experiment was conducted in the College of Agricultural Engineering at Al-Furat University in Deir Ez-Zor. The seeds of durum wheat (Triticum durum L., var. Sham 7) were soaked before planting in Petri dishes with two solutions: silver nanoparticles (Ag NPs) and regular silver nitrate (AgNO3) at four concentrations (10-20-40-80 ppm) in addition to soaking in distilled water (control treatment) for a duration soaking of 1,2 and 3 hours. Silver nanoparticles were synthesized by green methods from extract of dried Eucalyptus camaldulensis leaves and the resulting silver nanoparticles exhibit a spherical shape with dimensions of 12 nanometers as described by scanning electron microscopy (SEM). The surface plasmon resonance peak at a wavelength of 450 nm is based on UV-Vis spectroscopy. The results of seed soaking experiment indicated that the seed soaking of durum wheat before planting in silver nanoparticles (Ag NPs) or regular silver nitrate (AgNO3) for two hours had a significant effect on the studied germination indicators (germination rate, germination percentage, shoot length, root length, dry weight of shoots and roots). The values were higher when seeds were soaked in a 20 and 40 ppm of silver nanoparticles. The study recommends to soaking wheat seeds in a solution of silver nanoparticles at a concentration of 20-40 ppm for two hours before planting, also recommends to conducting several seed priming experiments with different nanomaterials for other varieties and crops.

Major concerns about the food security involving wheat production emerged when the conflict between Russia and Ukraine worsened because both countries were the main suppliers of wheat to 38 countries. This study aims to explore the competitiveness level of wheat production countries and the future exporters that may lead to global wheat production during the Russia-Ukraine crisis. This study analyzed the comparative advantages of the five largest wheat exporters from 2001 to 2021 using the revealed comparative advantage (RCA) and revealed symmetrical comparative advantage (RSCA) indices to examine the current level of wheat export competitiveness of the five major exporters. This study also predicts the three major wheat-producing countries (excluding Russia and Ukraine) using 83-month observations to forecast the autoregressive integrated moving average in the next six months. The findings disclosed that all major wheat countries were strongly competitive, and the forecast unveiled that Australia is capable to lead the wheat producing countries in the next six months. This evaluation was derived from the ARIMA approach’s forecast, demonstrating Australia to be statistically greater than the USA and Canada.

Originality/Value: Farmers must use sprinkler irrigation and avoid surface irrigation because it harms the soil and increases its salinity, also represents a waste of water resources.Research limitations: Limited availability of sufficient numbers of sprinkler irrigation devices in Iraq, their high prices, and the lack of experience of farmers to use them.The Purpose of the research is to evaluate the effect of the appropriate irrigation method and the amount of fertilizer specified for urea fertilizer that achieves the best growth for the plant and increase its productivity while avoiding a negative impact on soil properties.Research Method: Two types of irrigation: spraying and flooding, and four mineral fertilization treatments (0. 50.100. 150) kg.ha-1 were used in the work to study the apparent characteristics of wheat and the amount of productivity, using statistical analysis (Gen Stat) and simple correlation coefficient to find strength of association between variables. The experiment was carried out in 2023 at Al-Khatib Farm, located in Al-Numaniyah District, it is bordered to the south by Wasit Governorate, 50 km away, and to the north by Baghdad Governorate,160 km away (Iraq).Results and Discussion. Sprinkler irrigation showed significant differences with the surface irrigation in the length of the spike, number of seed per spike,1000 seed weight, number of spikes per square meter, grain yield kg .ha-1 and the harvest index, while the plant height was not significant. The study also showed that the correlation coefficient between the apparent characteristics of the plants was stronger in the case of using sprinkler irrigation compared to the surface irrigation.

Water stress is a major obstacle to agricultural production, significantly impacting both yield and quality. During the 2017–18 crop year in Shahriar region near Tehran, Iran, known for its dry and cold climate, a study was conducted to examine the influence of different irrigation levels on the quantitative and qualitative characteristics of various wheat cultivars. The experiment involved three irrigation levels: normal irrigation (control), withholding irrigation at the flowering stage, and withholding irrigation at the seed-filling stage as primary factors, with 21 different cultivars as secondary factors. The analysis showed that irrigation, cultivar type, and their interactions had a significant effect on grain yield, proline, total chlorophyll, carbohydrate content in the first and second internodes during flowering and ripening, as well as on SOD, CAT, GPX, MDA, DT, and D-OH-dG levels at a one percent significance level. Withholding irrigation at the flowering stage had a more severe impact compared to the seed-filling stage. The Rakhshan and Sivand cultivars yielded the highest and lowest grain yields under normal irrigation conditions, while the drought-tolerant Ofogh cultivar showed lower yields when irrigation was withheld at the flowering stage. Water stress led to increased proline levels and higher levels of SOD, CAT, MDA, DT, and D-OH-dG in wheat cultivars. The Rakhshan and Sivand cultivars were identified as drought-tolerant in this region.

This study compared the accuracy of various soil phosphorus assessment methods to measure the soil's ability to supply plants with phosphorus over a brief period in the field. Twenty individual soil samples were collected from a standing wheat (Triticum aestivum L) crop at depths ranging from zero to twenty centimeters. An equivalent plant spike sample was also procured from the soil sampling fields. In comparison to the wet acid digestion method used to detect phosphorus in plants, several methods were utilized to assess phosphorus in the soil, including resin extractable phosphorus, AB-DTPA extractable phosphorus, NaHCO3 extractable phosphorus, water-soluble phosphorus in suspension, and paste. The levels of variation and deficiency of phosphorus, which were found by different methods followed different patterns as shown by the fact that, AB-DTPA method finds phosphorus deficiency in 20% of samples while on the other hand, Olsen method finds phosphorus deficiency in 80% of samples. Even with such a small sampling area, none of the procedures showed a significant correlation with any other method that might account for uneven variation among the samples when determined by distinct procedures. However, corrections were observed to a certain degree between ammonium bicarbonate-diethylenetriaminepentaacetic acid (AB-DTPA) extractable and resin, as well as between other procedures and the plant P scale. Both resin and ammonium bicarbonate-diethylenetriaminepentaacetic acid (AB-DTPA) had a strong relationship with plant phosphorus, with the former showing a significant correlation of 0.48 and 0.21, respectively. Hence Resin and AB-DTPA methods are recommended for the determination of phosphorus under certain soil and plant conditions.

A two-year field experiment was conducted to measure the effects of densification methods on photosynthesis and yield of densely planted wheat. Inter-plant and inter-row distances were used to define rate-fixed pattern (RR) and row-fixed pattern (RS) density treatments. Meanwhile, four nitrogen (N) rates (0, 144, 192, and 240 kg N ha−1, termed N0, N144, N192, and N240) were applied with three densities (225, 292.5, and 360 × 104 plants ha−1, termed D225, D292.5, and D360). The wheat canopy was clipped into three equal vertical layers (top, middle, and bottom layers), and their chlorophyll density (ChD) and photosynthetically active radiation interception (FIPAR) were measured. Results showed that the response of ChD and FIPAR to N rate, density, and pattern varied with different layers. N rate, density, and pattern had significant interaction effects on ChD. The maximum values of whole-canopy ChD in the two seasons appeared in N240 combined with D292.5 and D360 under RR, respectively. Across two growing seasons, FIPAR values of RR were higher than those of RS by 29.37% for the top layer and 5.68% for the middle layer, while lower than those of RS by 20.62% for the bottom layer on average. With a low N supply (N0), grain yield was not significantly affected by density for both patterns. At N240, increasing density significantly increased yield under RR, but D360 of RS significantly decreased yield by 3.72% and 9.00% versus D225 in two seasons, respectively. With an appropriate and sufficient N application, RR increased the yield of densely planted wheat more than RS. Additionally, the maximum yield in two seasons appeared in the combination of D360 with N144 or N192 rather than of D225 with N240 under both patterns, suggesting that dense planting combined with an appropriate N-reduction application is feasible to increase photosynthesis capacity and yield.

Abstract. The effectiveness of mineral fertilization on wheat productivity was determined in a field experiment on Alluvial-meadow soil (Fluvisol, FAO 2015) at the experimental field of Tsalapitsa village, Plovdiv region, during 2019-2020. Four increasing rates of nitrogen fertilization along with a constant background of phosphorus fertilization were tested in the following variants: N40P100; N80P100; N120P100; N160P100, and N0P0 (control) – without any fertilization. The influence of these variants on yield and the content of basic macro- and microelements in wheat’s dry biomass were assessed. Plant samples were collected at the full maturity phase of wheat, with each variant sampled in triplicate, to determine the concentrations of N, P, K, Ca, Mg, Cu, Zn, Mn, and Fe. Correlation analysis and analysis of variance (ANOVA) were employed to identify dependencies between observed variables. Significant differences between the variants for N% content in grain, chaff and straw of wheat were established. The highest nitrogen values were observed in the grain of the N160P100 and N120P100 variants, at 2.10% and 2.02%, respectively. The applied fertilization had a significant positive effect on the accumulation of potassium, calcium, copper, manganese and iron in wheat biomass. This finding was supported by the correlation coefficients between fertilizer rates and N (R = 0.948), Ca (R = 0.854), Cu (R = 0.681), Mn (R = 0.735), and Fe (R = 0.769) in the wheat grain. Significant yield differences were observed among the variants, with the highest yield recorded for N120P100 (7123.0 kg/ha), more than tripling that of the control variant. The uptake of nitrogen, phosphorus and potassium by the total biomass of wheat increased depending on the fertilization rates and it was the highest in the N120P100 variant.

Introduction: Field wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field. Methods: There are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios. Results: Experiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information. Discussion: The findings demonstrate the practical potential of our framework for intelligent agriculture applications. [ABSTRACT FROM AUTHOR]

Wheat plant is one of the most basic food sources for the whole world. There are many species of wheat that differ according to the conditions of the region where they are grown. In this context, wheat species can exhibit different characteristics. Issues such as resistance to geographical conditions and productivity are at the forefront in this plant as in all other plants. The wheat species should be correctly distinguished for correct agricultural practice. In this study, a hybrid model based on the Vision Transformer (VT) approach and the Convolutional Neural Network (CNN) model was developed to classify wheat species. For this purpose, ResMLP architecture was modified and the EfficientNetV2b0 model was fine-tuned and improved. A hybrid transformer model has been developed by combining these two methods. As a result of the experiments, the overall accuracy performance has been determined as 98.33%. The potential power of the proposed method for computer-aided agricultural analysis systems is demonstrated. [ABSTRACT FROM AUTHOR]

Wheat landraces are traditional varieties that have evolved over generations in response to local environments and farming practices and therefore exhibit remarkable adaptability to challenging climatic conditions and low-input farming systems. While the suitability of Mediterranean landraces to non-optimal climatic conditions during anthesis and grain ripening stage have been previously assessed, the role of photosynthesis efficiency and stomatal control on this resilience remains unexplored. This study aims to evaluate the relationship between grain yield and the post-anthesis flag leaf gas exchanges of Sicilian wheat landraces under irrigated and rainfed conditions and to compare these traits to modern durum (Triticum turgidum subsp. durum) and bread wheat (T. aestivum) varieties. Results indicate that wheat landraces respond to water availability similarly to modern varieties, reducing stomatal conductance by 26.8% and net photosynthesis by 18.1% under rainfed conditions, resulting in 10.6% lower grain yield compared to irrigated conditions. However, some landraces demonstrate comparable or even higher flag leaf net photosynthesis rates and lower transpiration levels, leading to higher yields in both rainfed and irrigated conditions, confirming their value as a source of gene pool for wheat breeding programs in drought-prone Mediterranean regions. [ABSTRACT FROM AUTHOR]

Wheat and maize are two of the most dominant crops of considerable importance in human nutrition. At the same time, agriculture is responsible for about 1/3 of anthropogenic greenhouse gas emissions. The aim of this study is to estimate the carbon footprint of the two aforementioned important crops in the region of Larissa, Greece. Under this context, the emitted CO2eq- per kg of product, the corresponding emissions per hectare and per g of harvested plant protein were estimated. The carbon footprint was measured using the CoolFarm Tool (Cool Farm Alliance). Data were collected through questionnaires and interviews with local farmers. According to the results, the carbon footprint per hectare in maize (2,979 kg CO2eq- ha-1) is significantly higher compared to wheat (1,090 kg CO2eq- ha-1). In contrast, the differences in the footprints per kilogram of product and per g of harvested protein were insignificant. Tillage was found to be the main factor increasing CO2eq- emissions in both crops. In this research work we demonstrated that tillage regimes are crucial for mitigating agricultural related GHG emissions. [ABSTRACT FROM AUTHOR]

Challenges to crop sector arising from changing temperature and rainfall cause losses to food crop productivity in particular and agriculture sector in general. The present study aims to see the farm households' perception and adaptation to climate change and to determine the effects of adaptation on food crop productivity in Pakistan using cross-sectional data collected from Punjab and Sindh provinces of Pakistan. Probit model is used to evaluate farmer's understanding of climate change and its associated institutional, socio-economic and demographic variables. Results show that the majority of farmers have experienced the changes in rainfall (76%) and temperature (79%). Socio-economic and demographic variables such as education above primary schooling years, number of earners, medium farm size, NGOs, and access to information sources, namely radio and neighboring farmers, are significantly related with perception of climate change. Propensity score matching method (PSM) is employed for examining the factors influencing adaptation to climate change and the effects of adaptation on food crop productivity. Results of PSM indicate that male household heads, family size, matriculation education, households having access to credit, information source, land quality, perception of rainfall and rented land are significantly related with adaptation to climate change. Productivity gains for adapters are 0.04% and the productivity gains for non-adapters are 3.8% if they adapted. The study suggests the need to invest in farmer's education, improve information sources and skills of existing extension staffs on climate change and adaptation measures. The study posits to increase access to viable credit programs, enabling farmers to invest in adaptation strategies to cope with climate change. [ABSTRACT FROM AUTHOR]

Wheat plant is one of the most basic food sources for the whole world. There are many species of wheat that differ according to the conditions of the region where they are grown. In this context, wheat species can exhibit different characteristics. Issues such as resistance to geographical conditions and productivity are at the forefront in this plant as in all other plants. The wheat species should be correctly distinguished for correct agricultural practice. In this study, a hybrid model based on the Vision Transformer (VT) approach and the Convolutional Neural Network (CNN) model was developed to classify wheat species. For this purpose, ResMLP architecture was modified and the EfficientNetV2b0 model was fine-tuned and improved. A hybrid transformer model has been developed by combining these two methods. As a result of the experiments, the overall accuracy performance has been determined as 98.33%. The potential power of the proposed method for computer-aided agricultural analysis systems is demonstrated. [ABSTRACT FROM AUTHOR]

To optimize the application of plant growth-promoting rhizobacteria (PGPR) in field trials, tracking methods are needed to assess their shelf life and to determine the elements affecting their effectiveness and their interactions with plants and native soil microbiota. This work developed a real-time PCR (qtPCR) method which traces and quantifies bacteria when added as microbial consortia, including five PGPR species: Burkholderia ambifaria, Bacillus amyloliquefaciens, Azotobacter chroococcum, Pseudomonas fluorescens, and Rahnella aquatilis. Through a literature search and in silico sequence analyses, a set of primer pairs which selectively tag three bacterial species (B. ambifaria, B. amyloliquefaciens and R. aquatilis) was retrieved. The primers were used to trace these microbial species in a field trial in which the consortium was tested as a biostimulant on two wheat varieties, in combination with biochar and the mycorrhizal fungus Rhizophagus intraradices. The qtPCR assay demonstrated that the targeted bacteria had colonized and grown into the soil, reaching a maximum of growth between 15 and 20 days after inoculum. The results also showed biochar had a positive effect on PGPR growth. In conclusion, qtPCR was once more an effective method to trace the fate of supplied bacterial species in the consortium when used as a cargo system for their delivery. [ABSTRACT FROM AUTHOR]

Background: The rice-wheat cropping system is the prevailing agricultural method in the North-Western states of India, namely in the Indo-Gangetic plains. The practice of open burning of rice residue is frequently employed for expedient land preparation, but it has significant adverse impacts on both the environment and human health. These include the emission of greenhouse gases, loss of nutrients, elevated concentrations of particulate matter (PM), and disruption of the biological cycle. This research aims to investigate the implementation of effective management strategies in the rice-wheat cropping system, namely via the use of tillage-based crop cultivation techniques, stubble retention, and integration approaches. The objective is to enhance soil health features in order to augment crop yield and improve its attributes. Results: The research was carried out using a split plot experimental design, consisting of three replications. The main plot consisted of four different cultivation methods, while the subplot included three genotypes of both rice and wheat. The research demonstrates the enhanced efficacy of residue application is significantly augmenting soil nutrient concentrations compared to standard tillage practices (P < 0.05). This was accomplished by an analysis of soil nutrient levels, namely nitrogen (N), phosphorus (P), potassium (K), and organic carbon (OC), at a depth of 0–15 cm. The implementation of natural farming, zero tillage, and reduced tillage practices resulted in decreases in rice grain yields of 34.0%, 16.1%, and 10.8%, respectively, as compared to conventional tillage methods. Similarly, the implementation of natural farming, zero tillage, and reduced tillage resulted in reductions in wheat grain yields of 59.4%, 10.9%, and 4.6% respectively, in comparison to conventional tillage practices. Conclusion: Regarding the individual crop genotypes investigated, it was continuously observed that Him Palam Lal Dhan 1 and HPW 368 displayed considerably greater grain yields for both rice and wheat during the two-year experimental period. Furthermore, when considering different cultivation methods, conventional tillage emerged as the most effective approach for obtaining higher productivity in both rice and wheat. Additionally, Him Palam Lal Dhan 1 and HPW 368 exhibited superior performance in terms of various crucial yield components for rice (such as panicle density, grains per panicle, panicle weight, and test weight) and wheat (including effective tiller density, grains per spike, spike weight, and 1000-grain weight). [ABSTRACT FROM AUTHOR]

Background: Agropyron cristatum (L.) is a valuable genetic resource for expanding the genetic diversity of common wheat. Pubing3228, a novel wheat-A. cristatum hybrid germplasm, exhibits several desirable agricultural traits, including high grain number per spike (GNS). Understanding the genetic architecture of GNS in Pubing3228 is crucial for enhancing wheat yield. This study aims to analyze the specific genetic regions and alleles associated with high GNS in Pubing3228. Methods: The study employed a recombination inbred line (RIL) population derived from a cross between Pubing3228 and Jing4839 to investigate the genetic regions and alleles linked to high GNS. Quantitative Trait Loci (QTL) analysis and candidate gene investigation were utilized to explore these traits. Results: A total of 40 QTLs associated with GNS were identified across 16 chromosomes, accounting for 4.25–17.17% of the total phenotypic variation. Five QTLs (QGns.wa-1D, QGns.wa-5 A, QGns.wa-7Da.1, QGns.wa-7Da.2 and QGns.wa-7Da.3) accounter for over 10% of the phenotypic variation in at least two environments. Furthermore, 94.67% of the GNS QTL with positive effects originated from Pubing3228. Candidate gene analysis of stable QTLs identified 11 candidate genes for GNS, including a senescence-associated protein gene (TraesCS7D01G148000) linked to the most significant SNP (AX-108,748,734) on chromosome 7D, potentially involved in reallocating nutrients from senescing tissues to developing seeds. Conclusion: This study provides new insights into the genetic mechanisms underlying high GNS in Pubing3228, offering valuable resources for marker-assisted selection in wheat breeding to enhance yield. [ABSTRACT FROM AUTHOR]

Agronomy research traditionally relies on small, controlled trial plots, which may not accurately represent the complexities and variabilities found in larger, realworld settings. To address this gap, we introduce a Bayesian methodology for the analysis of yield monitor data, systematically collected across extensive agricultural landscapes during the 2020/21 and 2021/22 growing seasons. Utilizing advanced yield monitoring equipment, our method provides a detailed examination of the effects of green manure on wheat yields in a real-world context. The results from this comprehensive analysis reveal significant insights into the impact of green manure application on wheat production, demonstrating enhanced yield outcomes across varied landscapes. This evidence suggests that the Bayesian approach to analyzing yield monitor data can offer more precise and contextually relevant information than traditional experimental designs. This research underscores the value of integrating largescale data analysis techniques in agronomy, moving beyond small-scale trials to offer a broader, more accurate perspective on agricultural practices. The adoption of such methodologies promises to refine farming strategies and policies, ultimately leading to more effective and sustainable agricultural outcomes. The inclusion of a Python script in the appendix illustrates our analytical process, providing a tangible resource for replicating and extending this research within the agronomic community. [ABSTRACT FROM AUTHOR]

The effects of stress factors associated with climate change and agricultural management practices on microorganisms are often studied separately, and it remains to be determined how these factors impact the soil microbiome and, subsequently, plant growth characteristics. The aim of this study was to understand how the historical climate and agriculture to which soil microbes have been exposed can influence the growth characteristics of wheat seedlings and their associated bacterial communities. We collected soil from organic and conventional fields with different histories of climate conditions to extract microbes to inoculate wheat seeds under agar-based cultivation conditions. Within a growth period of 8 days, we monitored germination rates and time as well as seedling above-ground biomass and their associated bacterial communities. The results showed a positive interaction between conventional farming practices and an ambient climate for faster and higher germination rates. We demonstrate that soil microbial extracts from organic farming with experience of the future climate significantly enhanced above-ground biomass along with the diversity of bacterial communities associated with seedlings than other treatments. Such findings support the idea that organic agricultural practices not only mitigate the adverse effects of climate change but also promote the diversity of seedling-associated bacteria. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the impact of different crop production systems (organic, integrated, and conventional) on the yields of several spring wheat (Triticum aestivum L.) cultivars. A field experiment was carried out at the Agricultural Experimental Station of the Institute of Soil Science and Plant Cultivation in Osiny (Poland) in three consecutive growing seasons (2014, 2015, and 2016). Two factors were included in the experiment: the crop production system (organic, integrated, and conventional) and spring wheat cultivars (Kandela, Izera, Ostka Smolicka, and Waluta). The crop production system significantly differentiated the yield, health, and weed infestation of the spring wheat. Wheat yield in the conventional system (6.12 t·ha−1) was higher than in the organic system (3.68 t·ha−1) by 67%, whereas, in the integrated system (7.61 t·ha−1), it was greater than in the organic system by 109%. The lower yields in the organic system were mainly due to fewer ears per m2 and a smaller 1000-grain weight. In the organic system, we also observed a higher infestation of wheat by foliar fungal pathogens and weeds compared with the conventional and integrated systems. The spring wheat cultivars differed in yield structure and resistance to infestation by fungal pathogens. The Waluta and Izera cultivars performed well in all systems but yielded the best in the integrated and conventional ones. The Kandela cultivar was the most suitable for the organic system, as it achieved the highest yield (4.16 t·ha−1). This was mainly due to its ability to form a compact canopy with relatively high ear density, a large 1000-grain weight, and the highest resistance to fungal pathogens. The results for cultivars' performance in the organic system may be useful for farmers in decreasing yield gaps in relation to integrated and conventional systems. [ABSTRACT FROM AUTHOR]

The intensive agriculture that is used in many countries has led to a reduction in biodiversity and the deterioration of the environment. Therefore, it is important to increase the adoption of cropping systems with high biodiversity. The objectives of the present study were the following: 1. assess the performance and sustainability of novel highly diversified production systems compared to the current traditional system and 2. provide quantitative economic and ecosystem service information for farmers, extension workers, and policy makers in order to support the development of sustainable and resilient high species cultivar/landrace diversification (HSD) production systems. The rotation of wheat–pea–barley was a system with low energy inputs and high outputs, significantly increasing the energy efficiency. Also, the same system demonstrated better economic and environmental indices, making it a suitable cropping system for Mediterranean areas. [ABSTRACT FROM AUTHOR]

Techniques for classifying data using data mining are now a day prevalent in agriculture. The method of classifying seeds involves grouping various seed varieties according to their morphological characteristics. To accomplish categorization of the typical Charotar region (generally comprising Anand and Kheda districts of the Gujarat State of India) Gujarat Wheat (GW) varieties (TRITICUM – AESTIVUM) viz. GW 273, GW 496, GW 322, LOK-1, and GDW 1255 (TRITICUM – DURUM), Weka Explorer was used. The features used are area, perimeter, solidity, aspect ratio, major and minor axis of seed kernel, Hue, Saturation, Value, and SF1 (empirical). Features reduction was done using Information Gain (IG) and its modified version Gain Ratio (GR). This paper compares performance of Tree based data mining algorithms in classifying wheat varieties. For classification we used purely tree-based machine learning algorithms viz. J48, Random Forest, Hoeffding Tree, Logistic Model Tree (LMT), and REPTree. LMT- logistics regression method gives higher accuracy 96.4% compared to other classifiers. Hoeffding Tree classifiers stood second with 96% accuracy. For validation 10-fold cross validation was used. By reducing the number of folds in cross validation performance of most algorithms decreased except J48. The percentage of correctly classified instance increased for all algorithms when features were selected by GR except for J48. [ABSTRACT FROM AUTHOR]