Growth Stages Of Wheat Research Articles

Long-Term Conservation Tillage Increases Yield and Water Use Efficiency of Spring Wheat (Triticum aestivum L.) by Regulating Substances Related to Stress on the Semi-Arid Loess Plateau of China

Improving the water-use efficiency (WUE) of crops is the most effective way to increase yields in semi-arid regions. Field research was carried out based on a long-term experiment initiated in 2001, aimed to explore the mechanisms of different tillage practices effects on grain yield and WUE of spring wheat. Tillage practices in the research including conventional tillage (CT), no tillage with no straw mulching (NT), conventional tillage with straw incorporation (TS), and no tillage with straw mulching (NTS). The effects of tillage practices on soil’s physical and chemical properties, dry matter accumulation, grain yield, dynamics of stress-related substances, and WUE were observed. Soil and plant samples in this research were collected in 2020 (wet year), 2021 (dry year), and 2022 (dry year). The results indicated that NTS improved the soil’s physical and chemical properties. The NTS treatment had the lowest soil bulk and pH and the highest total N, NO3--N, and available P. Throughout the whole growth stage, soil water content in the NTS and TS treatments were significantly higher than that of CT by 8.77–20.40% and 2.19–18.83, respectively. Averaged catalase (CAT), peroxidase (POD), and soluble protein across the three years with NTS and TS were significantly increased by 1.26–25.52% compared to CT. Meanwhile, the NTS treatment had the lowest malondialdehyde (MDA) content among the different tillage practices. NTS maintained the highest dry matter accumulation throughout the whole growth stage among different treatments; it was increased by 10.47–73.33% compared with CT. The average grain yields and WUE of NTS across the three years were 6.09–30.70% and 6.79–40.55% higher than other tillage practices, respectively. It is concluded that NTS influences dry matter accumulation and water-use efficiency during the whole growth stage of spring wheat by improving the soil’s physicochemical properties and modulating spring wheat substances related to stress, which in turn promotes yield formation.

Changes in soil biological and chemical environment during wheat growth stages under nitrogen and straw management practices in rice-wheat system

Crop residues are a neglected source of sustainable agriculture since they are produced in large quantities, yet farmers frequently burn them. The purpose of this study was to assess the effects of nitrogen (N) application rate and residue management strategies for wheat production on changes in soil enzyme activity, microbial count, and chemical properties at various growth stages. Different combinations of N fertilizer rates, tillage, and residue management practices for wheat were investigated in split-plot design. The main plots were assigned for two N applications rates (N75 and N100, 75 and 100% recommended dose of N, whereas the subplots were assigned for seven treatments as: (1) conventional tillage (CT)-rice residue removed (R), (2) sowing with rotoseeder (RS)-R, (3) sowing with Happy Seeder (HS)+ loose rice residue removed (R50), (4) HS + 100% rice residue retained (R100), (5) RS + R50, (6) RS + R100, (7) CT + R100. The results revealed that N100 and HS + R100 increased wheat yield, chemical and biochemical properties, with the exception of polyphenol oxidase and peroxidase. All biochemical characteristics were greater during vegetative and reproductive growth stages than before seeding and harvesting. Dehydrogenase, polyphenol oxidase, and flourescein diacetate were found as the most sensitive indicators using principle component analysis.

From piggery wastewater to wheat using microalgae towards zero waste

Microalgae production is still expensive, driving the need to lower costs while strengthening the industry's environmental sustainability. Microalgae are recognized tools for efficient wastewater treatment, offering the recycling of nutrients and water for agriculture, and producing biomass rich in growth-promoting compounds to improve plant productivity and resistance to adverse conditions. The use of wastewater can reduce cultivation costs as it is a source of nutrients and water. Alternative low-cost methods can significantly decrease harvesting costs, which represents one of the most expensive steps of the whole process.The goal of this work was to evaluate the potential of wastewater-grown microalga biomass for agriculture purposes. To reduce production costs, the microalga Tetradesmus obliquus was produced in pre-treated photo-Fenton (PF) piggery wastewater in combination with the use of different harvesting techniques - electrocoagulation, flocculation, and centrifugation, and different combinations. From the wastewater treatment process, two fractions (biomass and supernatant) were evaluated for germination and growth of wheat (Triticum aestivum L.) plants and compared to non-harvested microalga culture (MC), distilled water, and Hoagland (synthetic) solution. The concentrated resulting from PF was also tested as a biofertilizer.The results confirm that both biomass and supernatants are useful for agricultural applications. The obtained biomass elicited a 20–105 % increase in germination index compared to the control, while supernatants were inhibiting. The opposite trend was observed at later stages of wheat growth, where the nutrient-enriched supernatants and the PF concentrate (PF-CC) increased the number of tillers (3–5) and leaves (30–42) after 83 days. Wheat plants treated with MC and PF-CC produced similar number of ears (3.4 ± 0.5 and 6.0 ± 4.1 ears per plant, respectively) than the synthetic control (5.7 ± 1.4) after 182 days. All fractions obtained from the process can be used in a zero-waste process.

Complex systems modelling of UK winter wheat yield

Wheat is one of the most important global crops, understanding the drivers of wheat yield has significant societal benefits. Climate variables are particularly important in determining interannual variations in wheat yield, either as primary factors which directly influence the stages of wheat growth, or as secondary factors through their influence on pests, diseases and soil conditions. Here we present a new approach to model wheat yield; an empirical method based on nonlinear complex systems identification, known as NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs model). We deploy the NARMAX analytical approach for a specific site, Rothamsted, UK, where detailed meteorological variables are available, together with specific information on site conditions and crop growth stages. NARMAX yield forecasts are compared with those from the WOFOST crop model and nine state-of-the-art machine learning (ML) models; experimental results show that NARMAX outperforms all the compared methods in both prediction accuracy and model interpretability. We also develop regional wheat yield forecasts derived from a new gridded meteorological data product.The NARMAX approach produces skillful forecasts (r = 0.78) of Rothamsted wheat yield for a validation period, with small errors. The NARMAX regional forecasts, based on less specific information than WOFOST, also show a high degree of skill (r = 0.73). In addition, the predictor terms chosen for the model are identifiable and can help to give insight into potential key processes involved in the determination of wheat yield at a specific location. This approach can be extended in principle to other crop types and locations. It is straightforward and inexpensive to implement, using a limited number of meteorological predictor variables, which can be taken from site-based observations, or from gridded meteorological datasets. The method is a new tool to understand the environmental drivers of wheat yields on an annual basis.

Characteristics of historical precipitation for winter wheat cropping in the semi-arid and semi-humid area.

Winter wheat (Triticum aestivum L.) is one of major crops in the area along Huai river, China where it is a semi-arid and semi-humid region with sufficient precipitation for an entire season, but with uneven distribution within various growth stages. The instability of precipitation is an important factor in limiting wheat production potential under climate change. Therefore, it is essential to characterise the precipitation associated with different crop developmental stages. Based on climate data from 1999 to 2020 in six representative meteorological stations, we characterised the historical precipitation relating to seven key growth stages in winter wheat. There is no clear trend of interannual variation of precipitation for wheat season, with an average of precipitation of 414.4 ± 121.2mm. In terms of the distribution of precipitation grade within a season, light rain was dominant. Continuous rain occurred frequently during the pre-winter seedling and overwintering stages. The critical period of water demand, such as jointing and booting, has less precipitation. The fluctuation range of precipitation in sowing, heading-filling and maturation stages is large, which means that there is flood and drought at times. In conclusion, these findings provide a foundation for instructing winter wheat cropping in confronting with waterlogging and drought risk due to uneven precipitation in 'Yanhuai' region, China.

Impact of Planting Date and Physiological Activator (Appetizer) Application on Quality and Quantity of Wheat Grains (Triticum aestivum.L)

A field experiment was conducted during the winter season 2022-2021 in the field experiments field of the College of Agriculture - Al-Qasim Green University in the south of Babylon province, located at latitude 32.40 north and longitude 44.39 east. In order to study the effect of planting date and stages of spraying with the physiological stimulant Appetizer on the growth and yield of wheat (Triticum aestivum L.)), the experiment was conducted by arranging the split plots according to the Randomized Complete Block Design (RCBD) with three replicates. The planting dates included the main plot T1, a suitable first date on 15/11 and T2 a second late date on 15/12, while the subplot included spraying with the physiological stimulant (Appetizer) according to the stages of wheat growth within the Zadoks classification, which was given the symbols (A0 without spraying and A1 stage, The results showed that the date of 11/15 was significantly superior to the studied traits, where the number of spikelets in spike was 20.1 spikelets. spike -1 The number of grains in the spike is 43.6 grain. spike -1 The number of spikes is 364. The weight of 1000 grains is 36.26 g, and the grain yield is 3.84 tons. ha1-. The biological yield is 11.25 tons. ha-1 and the carbohydrate content is 73.29%, while the date of 15/12 is significantly excelled on trait of the protein content, as the average was 9.43% As for the phases of spraying with the physiological activator (Appetizer), they were significantly excelled at the elongation and booting stage in the studied traits, including the number of spikes of 20.6 spikes. Plant-1 The number of grains is 48.9 grains. spikes -1 and the weight of 1000 grains is 40.7 g. The grain yield is 4.17 tons. hectare 1- and a biological yield of 11.01 tons. ha -1 The protein content is 9.56% and the carbohydrate content is 77.03%. As for the number of spikes, the number of spikes was significantly excelled at the tillering stage g and elongation, where their average values reached 368 spikes.m2. As for the interaction between planting dates and the phases of spraying with the physiological activator (Appetizer) it was significant in all the studied traits, the interaction between the date of 15/11 and the stages of spraying with the physiological activator at the elongation and end stage gave the highest average number of spikelets in the spike of 20.8 spikelets. spike -1 The number of grains in the spike is 52.7 grains. spike 1- and the weight of 1000 grains is 42.42 g. The grain yield is 4.66 tons. ha-1 has a biological yield of 11.46 tons. ha-1 and the carbohydrate content is 77.76%. As for the number of spikes, the interaction was significant between the date of 15/11 and the spraying phase when treating the tillering and elongation, as the average was 381 spikes. It reached 9.65%.

Effect of Planting Date and Spraying with the Physiological Stimulant (Appetizer) on the Growth and Yield of Wheat (Triticum aestivum.L)

A field experiment was conducted during the winter season 2022-2021 in the field experiments field of the College of Agriculture - Al-Qasim Green University in the south of Babylon province, located at latitude 32.40 north and longitude 44.39 east. In order to study the effect of planting date and stages of spraying with the physiological stimulant Appetizer on the growth and yield of wheat (Triticum aestivum L.)), the experiment was conducted by arranging the split plots according to the Randomized Complete Block Design (RCBD) with three replicates. The planting dates included the main plot T1, a suitable first date on 15/11 and T2 a second late date on 15/12, while the subplot included spraying with the physiological stimulant (Appetizer) according to the stages of wheat growth within the Zadoks classification, which was given the symbols (A0 without spraying and A1 stage, The results showed that the date of 11/15 was significantly excelled in the studied traits for plant height 83.24 cm, spike length of 11.82 cm, area of flag leaf 38.09 cm 2, chlorophyll content of 71.98, and number of grains in spike 43.6. grain. Spike -1 The number of spikes is 364. Spikes. m-2 The grain yield is 3.84 tons. ha-1 and the harvest index is 34.14%, As for the phases of spraying with the physiological stimulant Appetizer, they significantly excelled at the elongation and booting growth stage in the studied traits, including plant height 81.70 cm, spike length 11.80 cm, chlorophyll content 74.65, number of grains 48.9 grains, spike 1- and grain yield 4.17 tons. ha-1 and a harvest index of 37.75%. As for the area of the flag leaf and the number of spikes, they significantly excelled at the stage of tillering and elongation, as their mean values reached 40.08 cm2 and 368 spikes.m2 respectively. As for the interaction between the planting dates and the phases of spraying with the physiological stimulant (Appetizer), it was significant in all the studied traits. The interaction between the date of 15/11 and the phases of spraying with the physiological stimulant at the elongation and booting stage gave the highest average plant height of 84.14 cm and spike length of 12.57 cm. the content of chlorophyll and the number of grains in the spike is 52.7 grains. spike -1 and the grain yield is 4.66 tons. ha-1 and the harvest index is 40.64%. As for the area of the flag leaf and the number of spikes, the interaction was significant between the date of 11/15 and the spraying phase when treated at tillering and elongation averaged 45.29 cm2 and 381 spikes.m2 sequentially.

Impact of drought stress on spring wheat grain yield and quality

AbstractDrought stress (DS) at different growth stages of spring wheat (Triticum aestivum L.) may affect its grain yield and end‐use quality differently. The impact of drought stress at different growth stages on yield has been widely studied, but the impact on grain quality has not been thoroughly investigated. A 2‐year field experiment was conducted to evaluate the effects of DS at different growth stages on end‐use quality and grain yield in different spring wheat classes (soft white ‘Alturas,’ hard red ‘Coho,’ and hard white ‘Dayn’). The treatments included DS (from tillering to stem elongation, booting to heading, and anthesis to soft dough) plus a fully irrigated (well‐watered) check. We found significant differences between the three wheat classes in most end‐use quality parameters. Relative to the well‐watered conditions, DS from tillering to stem elongation and from booting to heading did not affect grain yield and most end‐use quality parameters. Flour protein, lactic acid and sucrose solvent retention capacities, and mixograph peak time were more impacted by post‐anthesis DS. These changes in end‐use quality were correlated and, thus, directly or indirectly affected baking performance. Our results indicate that reduced irrigation at early growth stages can maintain grain yield and end‐use quality of spring wheat of different classes, for example, grain yield of 437 g m−2 and flour protein of 13.2–16.4 g kg−1 across different classes subject to DS at early growth stages compared to 379 g m−2 and 13.9–17.9 g kg−1 subject to post‐anthesis DS.

Leaf wettability and leaf angle affect air-moisture deposition in wheat for self-irrigation

BackgroundClimate change and depleting water sources demand scarce natural water supplies like air moisture to be used as an irrigation water source. Wheat production is threatened by the climate variability and extremes climate events especially heat waves and drought. The present study focused to develop the wheat plant for self-irrigation through optimizing leaf architecture and surface properties for precise irrigation.MethodsThirty-four genotypes were selected from 1796 genotypes with all combinations of leaf angle and leaf rolling. These genotypes were characterized for morpho-physiological traits and soil moisture content at stem-elongation and booting stages. Further, a core set of ten genotypes was evaluated for stem flow efficiency and leaf wettability.ResultsBiplot, heat map, and correlation analysis indicated wide diversity and traits association. The environmental parameters indicated substantial amount of air moisture (> 60% relative humidity) at the critical wheat growth stages. Leaf angle showed negative association with leaf rolling, physiological and yield traits, adaxial and abaxial contact angle while leaf angle showed positive association with the stem flow water. The wettability and air moisture harvesting indicated that the genotypes (coded as 1, 7, and 18) having semi-erect to erect leaf angle, spiral rolling, and hydrophilic leaf surface (<90o) with contact angle hysteresis less than 10o had higher soil moisture content (6-8%) and moisture harvesting efficiency (3.5 ml).ConclusionsThese findings can provide the basis to develop self-irrigating, drought-tolerant wheat cultivars as an adaptation to climate change.

Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

Wheat Lodging Segmentation Based on Lstm_PSPNet Deep Learning Network

Lodging is one of the major issues that seriously affects wheat quality and yield. To obtain timely and accurate wheat lodging information and identify the potential factors leading to lodged wheat in wheat breeding programs, we proposed a lodging-detecting model coupled with unmanned aerial vehicle (UAV) image features of wheat at multiple plant growth stages. The UAV was used to collect canopy images and ground lodging area information at five wheat growth stages. The PSPNet model was improved by combining the convolutional LSTM (ConvLSTM) timing model, inserting the convolutional attention module (CBAM) and the Tversky loss function. The effect of the improved PSPNet network model in monitoring wheat lodging under different image sizes and different growth stages was investigated. The experimental results show that (1) the improved Lstm_PSPNet model was more effective in lodging prediction, and the precision reached 0.952; (2) choosing an appropriate image size could improve the segmentation accuracy, with the optimal image size in this study being 468 × 468; and (3) the model of Lstm_PSPNet improved its segmentation accuracy sequentially from early flowering to late maturity, and the three evaluation metrics increased sequentially from 0.932 to 0.952 for precision, from 0.912 to 0.940 for recall, and from 0.922 to 0.950 for F1-Score, with good extraction at mid and late reproductive stages. Therefore, the lodging information extraction model proposed in this study can make full use of temporal sequence features to improve image segmentation accuracy and effectively extract lodging areas at different growth stages. The model can provide more comprehensive reference and technical support for monitoring the lodging of wheat crops at different growth stages.

No-tillage with straw mulching promotes wheat production via regulating soil drying-wetting status and reducing soil-air temperature variation at arid regions

Straw returning is a technical measure of crop production widely adopted in arid and water-deficient areas. However, the mechanism by which straw returning drives spatio-temporal changes in soil moisture and temperature to improve crop productivity remains unclear. So, a three-year field experiment was conducted with four various straw returning treatments for wheat production: 1) no-tillage with straw mulching (NTSM), 2) no-tillage with straw standing (NTSS), 3) conventional tillage with straw incorporation (CTS), and 4) conventional tillage without straw returning (CT, control). The spatio-temporal variations of soil moisture content and soil temperature were assessed. It will provide the basis for selecting the appropriate way of straw returning to increase wheat yield and clarifying its hydrothermal regulation mechanism. NTSM and NTSS were effective in maintaining soil moisture and regulating soil temperature, and had a greater effect on grain increase, than CTS and CT. Meanwhile, NTSM had a relatively higher yield than NTSS through better regulation of soil hydrothermal characteristics. NTSM had higher soil moisture content in the 0–120 cm depth by 9.8–10.2%, 6.7–8.2%, 6.5–10.2%, and 6.9–7.4% at sowing to jointing, jointing to booting, booting to early-filling, and early-filling to maturing stages of wheat, respectively. NTSM decreased evapotranspiration modulus coefficient (ETMC) and evapotranspiration intensity (ETI) at jointing to early-filling stage of wheat, but increased both indexes from early-filing to maturing stage of wheat, which is helpful to adjust the contradiction of soil moisture demand for wheat in different growth stages. Additionally, NTSM had 9.8–10.2%, 6.7–8.2%, 6.5–10.2%, and 6.9–7.4% lower soil temperature in the 5–25 cm depth than CT, at the four aforementioned wheat growth stages, respectively. The difference of soil temperature was mainly reflected at the 5–15 cm depth by analyzing the difference on soil temperature in various soil depths. The treatment on NTSM had little variation and stabilized soil temperature, according to the difference between air and average soil temperature of 5–15 cm depth. NTSM allowed wheat to grow a suitable thermal environment across their growth period, which is a significant regulatory mechanism for wheat to maintain better growth and development. Thereby, NTSM boosted wheat grain yield by 18.6–27.3% as compared to CT. Collectively, the NTSM treatment had better effect on regulating soil drying-wetting status and reducing soil-air temperature variation, which could be used as a recommended measure to improve wheat productivity at arid conditions.

Availability Changes in Different Exogenous Selenium Fertilizers in Soil and Their Effects on Selenium Accumulation in Wheat

In order to understand the differences in the uptake and accumulation of several common exogenous selenium fertilizers by crops, a wheat pot experiment was conducted to study the availability changes in different selenium fertilizers (potassium selenate, potassium selenite, EDTA-chelated selenium, selenium powder, fly ash, and selenium-enriched straw) in soil and their effects on wheat growth and selenium uptake and distribution. The results showed that the change in availability of different exogenous selenium types in soil was different. During the whole growth period of wheat, the soil available selenium proportion of selenate, selenite, and EDTA-chelated selenium treatment was significantly higher than that of the control (CK), respectively, but there was no significant difference between the other treatments and the CK treatment. In the early stage of wheat growth, the soil available selenium proportion of selenate, selenite, and selenium powder treatment decreased gradually and tended to be stable in the later growth stage of wheat; however, the soil available selenium proportion of other exogenous selenium treatments showed a dynamic change of decreasing in the early period and increasing in the late period. The available selenium content in soil significantly affected the selenium uptake by wheat, and there was a significant positive correlation between them. Selenate application significantly increased the grain and leaf biomass of wheat, but other selenium fertilizers had no significant effect on wheat growth. The accumulation capacity of different exogenous selenium fertilizers for wheat followed the order of selenate>selenite, EDTA-chelated selenium>selenium powder, fly ash, and selenium-enriched straw. There was no significant difference between the selenium powder, fly ash, and selenium-enriched straw treatments and the CK treatment. Selenium was more easily transferred to and accumulated in the stems and leaves of wheat after the application of selenate, whereas selenium was more easily transferred to and accumulated in grains after the application of selenite and EDTA-chelated selenium.

Impact of simulated hail damage at different growth stages and canopy positions on rainfed and irrigated winter wheat

AbstractProducers and crop insurance providers will benefit from an accurate assessment of yield reduction after hailstorms that considers crop management, weather, growth stage, and impacted part of crop (hail affected canopy position). But, limited information is available. The main objectives of this study were to quantify wheat (Triticum aestivum L.) yield losses associated with simulated hail damage timing and canopy position. The study was conducted in two locations (Garden City [irrigated], and Manhattan [rainfed]) in Kansas, from 2018 to 2020 and 2016 to 2018, respectively. Treatments were an incomplete factorial combination of stem bending at seven flowering stages, above or below the flag leaf and a nontreated control. Results of the study indicated 11%–22% wheat yield reduction due to simulated hail damage above the flag leaf and 32%–52% due to hail impact below the flag leaf under irrigated conditions. Within the high‐yielding rainfed environment, hail damage caused up to 20% yield reduction compared with the control. In rainfed and low‐yielding environments, the impact of hail damage was mainly dependent on the growth stage of wheat at hail occurrence. Hail damage at Feekes 11.2–11.4 stages decreased yield by 20%–50% from the rainfed control. Hail damage at Feekes 10.0–10.5.4 decreased yield by 50%–70% from the rainfed control. We provide empirical evidence that indicates hail damage on wheat is dependent on the interaction effect of in‐season crop water management, weather, canopy position and crop growth stage, and estimates of yield loss due to hail should consider these factors.

Prediction of Winter Wheat Harvest Based on Back Propagation Neural Network Algorithm and Multiple Remote Sensing Indices

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators.

A COMPARISON OF TREE-BASED REGRESSION MODELS FOR SOIL MOISTURE ESTIMATION USING SAR DATA

Abstract. Soil moisture content plays a pivotal role in biomass development of vegetation coverage at various growth stages. Moisture content of the soil is considered as a crucial parameter for agricultural studies which directly leads to higher fertility rate. Remote sensing techniques, specifically Synthetic Aperture Radar (SAR) sensors, provides suitable opportunity for continuous soil moisture monitoring at various spatial and temporal resolutions. In this study, field campaigns conducted to measure soil surface parameters, including soil moisture and roughness, synchronized with Sentinel-1 pass over an agricultural region near Mohammadshahr, Iran. Fieldwork for soil moisture sampling have done during plants’ (canola and winter wheat) growth stages. The Gradient Boosted Regression Tree (GBRT), eXtreme Gradient Boosted (XGB), and Random Forest (RF) machine learning algorithms were employed to model the relationship between the ground measured soil moisture and polarimetric SAR derived features from Sentinel-1 imageries. The results showed promising results obtained for soil moisture estimation using the dual-polarized SAR dataset over crop-covered agricultural fields with R2 = 0.95 and RMSE = 0.023 m3 m−3 using the GBRT regression model.

Estimation of Relative Chlorophyll Content in Spring Wheat Based on Multi-Temporal UAV Remote Sensing

Relative chlorophyll content (SPAD) is an important index for characterizing the nitrogen nutrient status of plants. Continuous, rapid, nondestructive, and accurate estimation of SPAD values in wheat after heading stage can positively impact subsequent nitrogen fertilization management strategies, which regulate grain filling and yield quality formation. In this study, the estimation of SPAD of leaf relative chlorophyll content in spring wheat was conducted at the experimental base in Wuyuan County, Inner Mongolia in 2021. Multispectral images of different nitrogen application levels at 7, 14, 21, and 28 days after the wheat heading stage were acquired by DJI P4M UAV. A total of 26 multispectral vegetation indices were constructed, and the measured SPAD values of wheat on the ground were obtained simultaneously using a handheld chlorophyll meter. Four machine learning algorithms, including deep neural networks (DNN), partial least squares (PLS), random forest (RF), and Adaptive Boosting (Ada) were used to construct SPAD value estimation models at different time from heading growth stages. The model’s progress was evaluated by the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAPE). The results showed that the optimal SPAD value estimation models for different periods of independent reproductive growth stages of wheat were different, with PLS as the optimal estimation model at 7 and 14 days after heading, RF as the optimal estimation model at 21 days after heading, and Ada as the optimal estimation model at 28 d after heading. The highest accuracy was achieved using the PLS model for estimating SPAD values at 14 d after heading (training set R2 = 0.767, RMSE = 3.205, MAPE = 0.060, and R2 = 0.878, RMSE = 2.405, MAPE = 0.045 for the test set). The combined analysis concluded that selecting multiple vegetation indices as input variables of the model at 14 d after heading stage and using the PLS model can significantly improve the accuracy of SPAD value estimation, provides a new technical support for rapid and accurate monitoring of SPAD values in spring wheat.

Wheat thrips (Haplothrips tritici Kurd.) damage on grain crops in Uzbekistan

This article provides comprehensive insights into the detrimental impact of wheat thrips, a widespread and persistent pest that affects wheat crops within our country. The research delves into the conditions under which wheat thrips cause substantial harm to crops, taking into account the specific climatic factors present within our region. A comparative analysis of pest prevalence and the resulting damage was conducted between the years 2020 and 2023. The study underscores that wheat thrips infestation leads to significant harm across various growth stages of wheat, spanning from the tillering phase to the maturity phase. Through meticulous investigation, the research reveals that wheat thrips can be found at varying densities, ranging from 5 to 35 thrips per stem within typical agricultural fields. To enhance its content, the article is enriched with tables and images that illustrate the outcomes derived from the research. This visual and data-driven approach amplifies the understanding of the pest’s impact, its prevalence, and the associated damage it inflicts upon wheat crops.

Effects of nitrogen application rate on the accumulation of 13C assimilates after flowering and water-nitrogen use efficiency of wheat under supplemental irrigation based on soil moisture.

This study aimed to explore nitrogen fertilizer management measures to synergistically improve wheat yields and water and nitrogen use efficiency under supplemental irrigation based on soil moisture in the Huang Huai winter wheat area. Wheat variety "Yannong 1212" was used as the test material. There were three nitrogen application levels, 150 kg·hm-2 (N1), 210 kg·hm-2 (N2), and 270 kg·hm-2 (the conventional nitrogen application rate in the Huang Huai winter wheat area, N3), with the relative soil water content of 0-40 cm of each treatment was supplemented to 70% at the jointing and flowering stages. We investigated the effects of nitrogen rates on photosynthetic characteristics of flag leaves after flowering, 13C assimilate accumulation and transport, and water and nitrogen use efficiency after flowering of wheat. The results showed that photosynthetic capacity of flag leaves in the N2 and N3 was significantly higher than that in N1 14-35 days after flowering, and that there was no significant diffe-rence between N2 and N3 treatments. The 13C isotope tracing results showed that the translocation amount of 13C assimilates in vegetative organs in N2 was 12.1% and 7.1% higher than that in N1 and N3, respectively. The distribution amount of 13C assimilates in grains at maturity was 10.1% and 5.3% higher than that of N1 and N3, respectively. The amount of nitrogen fertilizer affected water consumption, water consumption proportion, and total water consumption in different growth stages of wheat. Water consumption during the whole growth period showed no difference between N2 and N3 treatments, but both were significantly higher than that for N1. Water consumption and water consumption proportion of N2 were higher from the jointing to maturity stages, water use efficiency of N2 was 7.5% and 4.8%, and grain yield was 4.7% and 10.9% higher than that of N3 and N1 treatments, respectively. The partial productivity of nitrogen fertilizer was 34.6% higher in the N2 than that of N3. Considering wheat grain yield and water and nitrogen use efficiency, 210 kg·hm-2 nitrogen application was the best rate under water-saving condition of supplementary irrigation after soil moisture measurement in the study area.

Распределение некоторых тяжелых металлов в озимой пшенице на разных стадиях её развития

In the modern world, with its rapid development of industry, environmental pollution by heavy metals is increasing on a scale uncharacteristic of nature. When heavy metals enter the soil in large quantities, they affect the biological and biochemical properties of soils, the change in the amount of mobile forms of nutrients in them. In polluted soils the absorption of heavy metals by plants leads to the accumulation of metals in edible parts of vegetables or crops. The total contamination of the soil is characterized by the gross content of heavy metals, and the availability of elements for plants is determined by their mobile forms. According to the calculation of the geoaccumulation index for the studied elements, the following series of metals have been compiled according to their contribution to soil pollution: Cu &gt; Zn &gt; Fe &gt; Mn. The quantitative measure of the accumulation intensity of chemical elements by plants from the soil is the accumulation coefficient, describing the degree of biofilicity of the elements, as well as intensity of their involvement in biological cycles. The accumulative abilities of roots and shoots in relation to zinc at different stages of wheat growth decrease in the following order: earing &gt; ripening &gt; tubing. However, for copper there is a gradual decrease in accumulation in the roots at different stages of wheat growth in the following order: earing &gt; tubing &gt; ripening; in shoots it is: ripening&gt; tubing &gt; earing. According to the results of the study, several series of metals have been worked out according to the ability of accumulation by winter wheat. The cal-culated value of the transition coefficient allows us to formulate the following conclusions: iron reaches the maximum value of the coefficient at the tubing stage (7.764) and has the greatest value among all elements and growth stages; zinc and manganese reach the maximum value of the coefficient at the earing stage (1.765 and 2.193, respectively); copper reaches it at the ripening stage (1.143).