Growth Stages Of Wheat Research Articles

Wheat growth stage identification method based on multimodal data

Accurate identification of crop growth stages is a crucial basis for implementing effective cultivation management. With the development of deep learning techniques in image understanding, research on intelligent real-time recognition of crop growth stages based on RGB images has garnered significant attention. However, the small differences and high similarity in crop morphological characteristics during the transition between adjacent growth stages pose challenges for accurate identification. To address this issue, this study proposes a multi-scale convolutional neural network model, termed MultiScalNet-Wheat (MSN-W), which enhances the algorithm's ability to learn complex features by utilizing multi-scale convolution and attention mechanisms. This model extracts key information from redundant data to identify winter wheat growth stages in complex field environments. Experimental results show that the MSN-W model achieves a recognition accuracy of 97.6 %, outperforming typical convolutional neural network models such as VGG19, ResNet50, MobileNetV3, and DenseNet. To further address the difficulty in recognizing growth stages during transition periods, where canopy morphological features are highly similar and show small differences, this paper introduces an innovative approach by incorporating sequential environmental data related to wheat growth stages. By extracting these features and performing multi-modal collaborative inference, a multi-modal feature-based wheat growth stage recognition model, termed MultiModalNet-Wheat (MMN-W), is constructed on the basis of the MSN-W model. Experimental results indicate that the MMN-W model achieves a recognition accuracy of 98.5 %, improving by 0.9 % over the MSN-W model. Both the MSN-W and MMN-W models provide accurate methods for observing wheat growth stages, thereby supporting the scientific management of winter wheat at different growth stages.

Detecting Water Stress in Winter Wheat Based on Multifeature Fusion from UAV Remote Sensing and Stacking Ensemble Learning Method

The integration of remote sensing technology and machine learning algorithms represents a new research direction for the rapid and large-scale detection of water stress in modern agricultural crops. However, in solving practical agricultural problems, single machine learning algorithms cannot fully explore the potential information within the data, lacking stability and accuracy. Stacking ensemble learning (SEL) can combine the advantages of multiple single machine learning algorithms to construct more stable predictive models. In this study, threshold values of stomatal conductance (gs) under different soil water stress indices (SWSIs) were proposed to assist managers in irrigation scheduling. In the present study, six irrigation treatments were established for winter wheat to simulate various soil moisture supply conditions. During the critical growth stages, gs was measured and the SWSI was calculated. A spectral camera mounted on an unmanned aerial vehicle (UAV) captured reflectance images in five bands, from which vegetation indices and texture information were extracted. The results indicated that gs at different growth stages of winter wheat was sensitive to soil moisture supply conditions. The correlation between the gs value and SWSI value was high (R2 > 0.79). Therefore, the gs value threshold can reflect the current soil water stress level. Compared with individual machine learning models, the SEL model exhibited higher prediction accuracy, with R2 increasing by 6.67–17.14%. Using a reserved test set, the SEL model demonstrated excellent performance in various evaluation metrics across different growth stages (R2: 0.69–0.87, RMSE: 0.04–0.08 mol m−2 s−1; NRMSE: 12.3–23.6%, MAE: 0.03–0.06 mol m−2 s−1) and exhibited excellent stability and accuracy. This research can play a significant role in achieving large-scale monitoring of crop growth status through UAV, enabling the real-time capture of winter wheat water deficit changes, and providing technical support for precision irrigation.

Effect of sulfur and potassium foliar applications on wheat grain protein quality

ContextThe effects of sulfur and potassium fertilizers on wheat yield and quality have been well studied, but most of them are used as basal fertilizers. However, the root system is senescent at the later stages of wheat growth and cannot absorb sufficient sulfur and potassium fertilizers plant needed. It is still unclear whether sulfur and potassium foliar applications at the later stages can affect wheat yield and quality. ObjectiveThis experiment was conducted to investigate the changes of sulfur and potassium accumulation and transport, protein synthesis and flour processing quality after foliar application of sulfur and potassium fertilizers, transport and the relating physiological mechanisms. MethodsIn 2020–2021 and 2021–2022, a three-factor split plot experiment was carried out in the middle and lower reaches of Yangtze River, with wheat variety, concentration of sulfur fertilizer and potassium fertilizer served as main factor, subfactor and sub-subfactor respectively. ResultsIn the study from 2020 to 2022, the albumin protein content of both varieties decreased in the nutrient spraying treatment, particularly in the high potassium level. However, there was no significant effect on the globulin content. Moreover, the gliadin protein content decreased in the sulfur spraying treatment alone, but significantly increased the wheat gluten content, and thereby the total protein content. We also found significant genotypic differences in the composition and content of high molecular weight wheat glutenin subunits (HMW-GS) between the two varieties. The foliar spraying of sulfur and potassium fertilizers significantly increased the content of subunits 1 and 8 in Yangmai (YM15) and subunits 7 and 12 in Yangmai (YM16), and the effect of mixed spraying was better than single-nutrient spraying, especially the amount of 0.2 % sulfur and 0.3 % potassium fertilizer treatment. ConclusionsThe superimposed effect of sulfur and potassium effectively increased the total protein content by promoting the accumulation of sulfur in transit to the seeds, increasing the substrate supply level, and enhancing enzyme activity. Although sulfur and potassium combination can improve the grain protein quality and flour processing quality, too high spraying concentrations can decrease the flour processing quality. ImplicationsSpraying the appropriate amount of 0.2 % sulfur and 0.3 % potassium fertilizer can serve as an optimization measure for high-quality and efficient production of different types of wheat varieties.

Early estimation of glutelin to gliadin ratio in wheat grain using high-dimensional and hyperspectral reflectance

Precise and timely estimation of the glutelin-to-gliadin ratio (Glu/Gli) in wheat grain is pivotal for crop monitoring, as it is a crucial quality indicator ensuring the production of high-quality wheat flour. Despite the recognized potential of hyperspectral technology in crop phenotype estimation, its application to estimate Glu/Gli in wheat grains faces challenges due to complex spectral-chemical relationships and the influence of growing seasons. This study addresses this gap by cultivating 11 wheat varieties and collecting high-dimensional hyperspectral data from field experiments during various growth stages of wheat (2018–2019 and 2019–2020). Utilizing vegetation indices (VIs) in conjunction with linear mixed-effects model (LMM) and random forest regression model (RFR), it constructs a robust Glu/Gli estimation model (with a Glu/Gli range of 1.063 to 2.218). Results reveal that a singular VI application suffers from data limitations, while the integration of multiple VIs significantly enhances estimation accuracy. The mid-grain filling period emerges as a critical stage for accurate Glu/Gli estimation, with TCARI (transformed chlorophyll absorption reflectance index) demonstrating notable significance and high correlation. In model performance, RFR (R2 = 0.691, rRMSE = 0.096, RPD = 1.872, RER = 6.028) outperforms LMM (R2 = 0.477, rRMSE = 0.131, RPD = 1.383, RER = 4.453), exhibiting superior accuracy in estimating grain Glu/Gli for diverse wheat varieties. This study introduces a rapid and accurate approach for early wheat grain Glu/Gli estimation, offering valuable insights for wheat value chain and precision farming.

Spatial variability and uncertainty associated with soil moisture content using INLA-SPDE combined with PyMC3 probability programming

Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing data-driven model. Grid samples of SVMC covered approximately a 3-ha field during the jointing growth stage of winter wheat, and SVMC were measured by Time Domain Reflectometry (TDR), located in North China Plain, China. Bayesian inference was performed to explore spatial heterogeneity, robustness, transparency, interpretability and uncertainty related to SVMC using python-based PyMC3 combined with Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) model. The results showed that the prediction surface of SVMC, the lower and upper limits of 95% credible intervals quantified uncertainty associated with SVMC, cauchy prior of the flexibility and adaptability to obtain state-of-the-art predictive performance is more robust than gaussian prior for SVMC prediction, the transparency and interpretability of SVMC prediction model were revealed by MCMC (Markov-Chain Monte-Carlo) trace plots, KDE (Kernel density estimates), and rank plots. The uncertainty associated with SVMC can explicitly be described using the highest-posterior density interval, the prediction lower and upper limits.

YOLOv5s-Based Image Identification of Stripe Rust and Leaf Rust on Wheat at Different Growth Stages.

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.

Impact of Stage-Specific Irrigation on the Growth and Yield of Wheat

Stage-specific irrigation is essential for ensuring optimal water use at critical growth stages in wheat (Triticum aestivum), leading to increased yield and improved grain quality. Therefore, determining an appropriate irrigation schedule for wheat is crucial for enhancing productivity. Keeping this view in mind, an experiment was conducted at the farmer’s fields of Dinajpur Sadar Upazila, Dinajpur, Bangladesh from November 2020 to April 2021, aimed to evaluate the impact of irrigation schedules on the growth and yield of wheat (BARI Gom-33). A randomized complete block design (RCBD) with seven treatments and three replications was implemented. Treatments included varying irrigation timings: T0 (no irrigation), T1 (one irrigation at maximum tillering), T2 (one irrigation at booting), T3 (one irrigation at heading), T4 (two irrigations at maximum tillering and heading), T5 (milking dough), and T6(two irrigations at booting and milking dough). Key metrics assessed included plant height, leaf count, tiller number, dry matter, growth stages, grain number, thousand grain weight, and overall yield at different days’ intervals. Results indicated that irrigated treatments (T1-T6) significantly improved the parameters for growth and yield compared to the control (T0). Notably, T4, with two irrigations, yielded the highest grain production at 5.20 t/ha, followed closely by T6. These findings highlight the significance of proper irrigation scheduling for optimizing wheat yield, indicating that applying two irrigations at the maximum tillering and heading stages can significantly enhance agricultural productivity.

Effect of Initial Inoculum on the Temporal and Spatial Dynamics of Wheat Blast Under Field Conditions in Bolivia.

Epidemiological studies to better understand wheat blast (WB) spatial and temporal patterns were conducted in three field environments in Bolivia between 2019 and 2020. The temporal dynamics of wheat leaf blast (WLB) and spike blast (WSB) were best described by the logistic model compared with the Gompertz and exponential models. The nonlinear logistic infection rates were higher under defined inoculation in experiments two and three than under undefined inoculation in experiment one, and they were also higher for WSB than for WLB. The onset of WLB began with a spatial clustering pattern according to autocorrelation analysis and Moran's index values, with higher severity and earlier onset for defined than for undefined inoculation until the last sampling time. The WSB onset did not start with a spatial clustering pattern; instead, it was detected later until the last sampling date across experiments, with higher severity and earlier onset for defined than for undefined inoculation. Maximum severity (Kmax) was 1.0 for WSB and less than 1.0 for WLB. Aggregation of WLB and WSB was higher for defined than for undefined inoculation. The directionality of hotspot development was similar for both WLB and WSB, mainly occurring concentrically for defined inoculation. Our results show no evidence of synchronized development but suggest a temporal and spatial progression of disease symptoms on wheat leaves and spikes. Thus, we recommend that monitoring and management of WB should be considered during early growth stages of wheat planted in areas of high risk.

Optimization of a Boom Height Ultrasonic Detecting Model for the Whole Growth Cycle of Wheat: Affected by the Oscillation of the Three-Section Boom of the Sprayer

In the dynamic operation of a boom sprayer, the boom oscillation will cause the detection value of the boom height to fluctuate greatly, resulting in failures of the control system. Based on the previously developed static boom height detection model for the entire wheat growth cycle, this study aimed to optimize the model to reduce the impact of boom oscillation on the accuracy of boom height detection. Three ultrasonic sensors were installed on each section boom of a three-section boom sprayer, and dynamic boom height detection tests were conducted at vehicle speeds of 4 to 8 km/h across six growth stages of winter wheat in Beijing, a total detection area within a single fixed operational row of approximately 14 ha. The test results showed that as vehicle speed increased, boom oscillations intensified across all sections. By setting the boom oscillation correction parameters, the detecting value of each section of boom height is corrected. The results show that the fluctuation and deviation degree of the boom height-detecting value are obviously reduced, and the correction effect is obvious. Further analysis of the detecting value of the boom height after the correction shows that the previously established detection model still maintains high detection accuracy under dynamic conditions; that is, the detection position of the ultrasonic sensor does not downward shift. This paper provides a low-cost technical method that can be directly applied to the dynamic detection of boom height.

Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data.

Crop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling. The current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height. This study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model's estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model's R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93. In conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

Exploring the Effects of Irrigation Schedules on Evapotranspiration Partitioning and Crop Water Productivity of Winter Wheat (Triticum aestivum L.) in North China Plain Using RZWQM2

ABSTRACTA significant basis for winter wheat production in China is the North China Plain (NCP). However, winter wheat production is severely hampered by water shortages in this area. Transpiration co‐occurs with photosynthesis, affecting crop water productivity (CWP). The purpose of this experiment is to use Root Zone Water Quality Model (RZWQM2) to study actual transpiration (AT) and evaporation (AE) under different irrigation schedules and then determine its impact on grain yield and CWP. In the 2019–2022 winter wheat growing seasons, four experimental treatments were set up: no irrigation during growth period (I0), irrigation at jointing stage (I1), irrigation at jointing and anthesis stage (I2) and irrigation at jointing, anthesis and filling stage (I3), and the RZWQM2 model was calibrated and verified in this experiment. A higher yield (7840.90 kg/ha for an average of 3 years) and the highest CWP can be obtained in I2 treatment (increased by 12.72%, 5.98% and 4.28% for an average of 3 years, respectively, compared to the other three treatments). The model has a good simulation effect on soil water dynamic change and plant physiological performance of the four treatments; the model showed that irrigation increased the simulated AE and AT; however, reduced AE/actual evapotranspiration. For the whole growth period, AT in I3, I2, I1 and I0 was 351.70, 317.30, 271.50 and 223.70 mm, respectively. Especially in the late growth stage of winter wheat, the AT in I3 was 65.20 mm for an average of 3 years, which was significantly higher than I2, I1 and I0 by 31.60, 13.50 and 10.00 mm, respectively. Thus, I3 increased AT at the late growth stage of winter wheat and resulted in an increase in grain yield; however, it did not significantly increase CWP. This study demonstrated that irrigation at winter wheat jointing and anthesis stages can improve the CWP to achieve the goal of stable grain yield and water saving.

Long-term application of FYM and fertilizer N improve soil fertility and enzyme activity in 51st wheat cycle under pearl millet-wheat

Our study from an ongoing research experiment initiated in Rabi 1967 at the Research Farm of CCS Haryana Agricultural University, Haryana, India, reports that during the 51st wheat cycle in pearl millet-wheat sequence, adding FYM in both seasons significantly impacted various soil parameters at different wheat growth stages compared to the rabi season. The application of 15 t of FYM ha−1 resulted in a considerable increase in dissolved organic carbon content (9.1–11.2%), available P (9.7–12.1%), and available S (12.6–17.1%), DHA levels by 7.3–22.0%, urease activity (10.1 and 17.0%), β-Glucosidase activity (6.2–8.4%), and APA activity (5.2–10.6%), compared to 10 t FYM ha−1. Application of N120 exhibited a considerable improvement in DHA (11.0–23.2%), β-Glucosidase (9.4–19.2%), urease (13.3–28.3%), and APA (3.3–6.2%) activity compared to control (N0). At stage 3, the box plot revealed that 50% of the available N, P, and S values varied from 223.1 to 287.9 kg ha−1, 53.0 to 98.2 kg ha−1, and 50.0 to 97.6 kg ha−1, respectively. Principal component analysis, with PC1 explaining 94.7% and PC2 explaining 3.15% of the overall variability, and SOC had a polynomial relationship with soil characteristics (R2 = 0.89 to 0.99). Applying FYM15 × N120 treatment during both seasons proved beneficial in sustaining the health of sandy loam soil in North-West India.

Monitoring wheat area using sentinel-2 imagery and In-situ spectroradiometer data in heterogeneous field conditions

Crop statistics are crucial for developing a demand-based export and import strategy to ensure a country’s sustainable food security. Remote sensing efficiently generates essential crop statistics, while ground-based supplementary sensor data offers sufficient information for crop delineation. This study explored the multispectral satellite imagery using in-situ ground-based hyperspectral reflectance phenology information as training data to delineate wheat from other competitive winter crops in Northwestern Bangladesh as a case study. Wheat spectral signatures were primarily obtained through a hand-held Spectroradiometer at various phenological stages, aligned with Sentinel-2 data availability. Five vegetation indices (VIs), namely Normalized Difference Vegetation Index (NDVI), Red-edge NDVI (RENDVI), Enhanced Vegetation Index (EVI), Greenness Chromatic Coordinate (GCC) and Soil-Adjusted Vegetation Index (SAVI), were derived from Spectroradiometer-data across six wheat growth stages: seedling, tillering, booting, flowering, grain development, and maturity. Maximum and minimum threshold values for the VIs at those six growth stages were determined from regression analysis of the values collected from Spectroradiometer and Sentinel-2. A rule-based classification technique was then used to categorize Sentinel-2 for wheat crop delineation based on those threshold values. The results revealed that maps based on NDVI, EVI, and SAVI showed overall accuracies of 83.33%, 85.18%, and 81.48%, respectively. These accuracies were found to be statistically acceptable (p < 0.05) outcomes. A positive agreement was observed when comparing the remotely sensed area at the union (4th tier administrative level) with the officially reported data of Bangladesh. This innovative method has the potential to be extended for developing phenology and area delineation for other major crops locally and globally.

Temporal and spatial patterns of extreme heat on wheat in China under climate change scenarios

Revealing the spatial-temporal pattern of extreme heat on staple crops is crucial for proposing adaptation strategies to mitigate climate change-related agricultural risks. Studies in this field generally focus on the reproductive stage and rely on a single-staged threshold temperature to construct extreme heat indicators, which particularly neglect the vegetative stage of wheat. Therefore, to measure the extreme heat risks more scientifically across the entire life cycle of wheat, our study defines a new comprehensive extreme heat index (CEHI) that considers specific thresholds in both the reproductive and vegetative stages. In general, under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5), approximately 20 % of the wheat-planting regions in China, especially in winter wheat regions such as the North China Plain, the Sichuan Basin, and the Xinjiang Tarim Basin, are projected to face high levels of extreme heat. Meanwhile, from 2010 to 2099, the average growth rates of extreme heat in China under RCP2.6, RCP4.5, and RCP8.5 scenarios are approximately 0.08, 0.06, and 0.1, respectively. By the century's end, the proportion of wheat-planting regions experiencing high and very high levels (CEHI≥0.4) of extreme heat is projected to increase from 18.0 %, 17.9 %, and 18.4 % to 21.4 %, 25.1 %, and 28.9 % under RCP2.6, RCP4.5, and RCP8.5 scenarios. Among them, RCP8.5 has the highest extreme heat severity on wheat in China, followed by RCP4.5, while RCP2.6 has minimal severity. Under the RCP8.5 scenario, the proportions of very high, high, moderate, low, and very low levels of extreme heat are 3.4 %, 18.5 %, 16.7 %, 14.9 %, and 46.5 %, respectively. Meanwhile, our study also emphasizes that although higher-latitude spring wheat regions will experience a significantly increasing trend in extreme heat, this may not spell long-term damage to wheat. Therefore, with consideration of varied temperature sensitivities across wheat growth stages, our study indicates that CEHI serves as an effective method to comprehensively and scientifically assess extreme heat on wheat. Furthermore, based on the regional and varietal differences in extreme heat under climate change, our study highlights the importance of developing region- and variety-specific policies to ensure the sustainability of wheat.

Projecting Climate Change Impact on Precipitation Patterns during Different Growth Stages of Rainfed Wheat Crop in the Pothwar Plateau, Pakistan

In rainfed areas, precipitation variations directly impact wheat growth stages such as emergence, tillering, jointing and booting, and maturity. Evaluating the impact of climate change on precipitation patterns during these critical growth stages is crucial for adapting climate change and ensuring global food security. In this study, projections of five General Circulation models (GCMs) under two shared socioeconomic pathways (SSPs) were used to predict the changing characteristics of precipitation during four main growth stages of wheat in the rainfed region of the Pothwar Plateau, Pakistan. Historical datasets of daily precipitation at six weather stations were analyzed to check the past changes in the precipitation patterns. During the baseline period (1985–2014), the annual average precipitation decreased at a rate of −9.75 mm/decade, while the amount of precipitation during the rabi season (wheat-growing season) decreased at a rate of −20.47 mm/decade. An increase in the precipitation was found during the fourth (flowering) stage of crop growth, while the first three stages experienced a decrease in the precipitation amount. The multimodal ensembled data, under the SSP2-4.5 scenario, revealed a significant decline (at the rate of −16.63 mm/decade) in the future annual precipitation. However, it is projected that, under SSP2-4.5, there may be a slight increase (4.03 mm/decade) in the total precipitation amount during the future rabi season. Under the SSP5-8.5 scenario, average annual precipitation exhibited a slightly increasing trend, increasing by 1.0 mm/decade. However, during the rabi season, there was a possibility of a decrease in precipitation amount, with a rate of 11.64 mm/decade. It is also expected that the precipitation amount may vary significantly during the crown root initiation, jointing and booting, and flowering stages in the near future. These results provide a framework for the planning of wheat production in the Pothwar region of Pakistan, taking into account the potential impact of shifting weather patterns, particularly in terms of uneven precipitation.

Drought Characteristics and Causes during Winter Wheat Growth Stages in North China

Drought Characteristics and Causes during Winter Wheat Growth Stages in North China

Wheat Crop Management and growth stage monitoring in some gypsiferous soil units using remote sensing

The study used remote sensing to manage and monitor wheat crop health in some gypsiferous soil units. Five sites cultivated with wheat and irrigated by a central pivot irrigation system were selected within gypsiferous soil units in some agricultural lands. Soil and plant samples were collected at the best spectral and vegetative growth stage (grain filling stage) from each site. Three samples of the plant and soil were collected with three replicates, resulting in a total of (5 × 3 × 3 = 45 samples for both soil and plant). Samples were prepared for conducting laboratory analyses Satellite imagery of the OLI type from the Landsat 8 satellite, acquired on 24/2/2020, was used to calculate the following spectral indices: NDVI, SAVI, OSAVI, GOSAVI, GDVI, NDMI, CMFI, LAI. The results showed a variation in the concentration of fertile elements in the soil and plants between the study sites, with the third site relatively outperforming the other sites. On the other hand, we observed a variation in the values of spectral indices between the study sites for all the spectral indices and an increase in values with the progress and increase in the size of the canopy cover to reach the best spectral growth stage at the stage of ear fullness, which is the stage of spectral stability and appropriate for monitoring crop productivity and assessing plant health. The results also concluded that NDVI and LAI are among the most important pieces of evidence that have a strong relationship with plant density and estimation of its general condition, as the relationship was strong logarithmic and linear with nitrogen, as the values of R2 reached 0.91 and 0.88, respectively, and the value of the coefficient of determination R2 reached with The phosphorus concentration was 0.67 and 0.67, respectively. Also, the use of spectral indices depends on the spectral bands that fall at the wavelength of 0.6-0.7 micrometers, which is the region where a high absorption process occurs if the vegetation cover is intact and healthy, especially the NDVI index.

Impact of Skipping Irrigation at Different Growth Stages of Wheat on Crop Water Productivity in North Nile Delta 1- Vegetative Growth and Water Relationships

Impact of Skipping Irrigation at Different Growth Stages of Wheat on Crop Water Productivity in North Nile Delta 1- Vegetative Growth and Water Relationships

Winter Wheat Aboveground-Biomass Estimation and Its Dynamic Variation during Coal Mining—Assessing by Unmanned Aerial Vehicle-Based Remote Sensing

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

Amelioration of drought stress in wheat by using plant growth-promoting rhizobacteria and biogas slurry

&lt;p&gt;Drought stress has a significant impact on cereal-based staple food production, particularly in developing countries like Pakistan. To ensure a sustainable and reliable food supply, it is essential to develop comprehensive production plans that incorporate various approaches to mitigate the effects of drought. In a study conducted using a randomized complete block design, we investigated the potential of plant growth-promoting rhizobacteria (PGPR) and biogas Slurry (BGs) either individually or in combination to alleviate drought stress at different stages of wheat growth. The two-year field research demonstrated that the application of Azosprillium lipoferum with biogas slurry resulted in improved water relations, chlorophyll content, grain quality, yield, and related characteristics in wheat plants compared to the stressed treatments. Particularly, the combined treatment of PGPR and BGs exhibited the most favorable outcomes. Notably, the combined treatment effectively mitigated drought stress by significantly increasing antioxidant levels (17% APX, 29% POD, 34% CAT, and 41% SOD) during the grain-filling stage (GFS) compared to the controls. The combined treatment resulted in a remarkable 40% improvement in the respective controls at the GFS stage. Overall, the combined use of PGPR and BGs was identified as an effective strategy to enhance the resilience of wheat plants to drought, particularly in arid and semi-arid regions.&lt;/p&gt;