Wheat Fields Research Articles

Pixel to practice: multi-scale image data for calibrating remote-sensing-based winter wheat monitoring methods

Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availability of accurate spatial information on crop growth, vigor, and health status at large scales. Challenges persist particularly in interpreting remote sensing signals within commercial crop production due to the variability in canopy appearance resulting from diverse factors. Recently, high-resolution imagery captured from unmanned aerial vehicles has shown significant potential for calibrating and validating methods for remote sensing signal interpretation. We present a comprehensive multi-scale image dataset encompassing 35,000 high-resolution aerial RGB images, ground-based imagery, and Sentinel-2 satellite data from nine on-farm wheat fields in Switzerland. We provide geo-referenced orthomosaics, digital elevation models, and shapefiles, enabling detailed analysis of field characteristics across the growing season. In combination with rich meta data such as detailed records of crop husbandry, crop phenology, and yield maps, this data set enables key challenges in remote sensing-based trait estimation and precision agriculture to be addressed.

Ionome composition influence wheat yield on saline and calcareous soils: the case of Triticum aestivum L. var. ‘Sirvan’

Soil pH and salinity significantly affect plant biogeochemical safety, ionome, and nutritional balance. Under such conditions, we established local compositional nutrient diagnosis standards using the centered log ratio (CNDclr*) means and standard deviations of the ionome from high-yield and nutritionally balanced ‘Sirvan’ wheat specimens (Triticum aestivum L. var. ‘Sirvan’). Critical nutrient indices (I2 X) and the critical yield of the wheat fields were determined based on the Cate-Nelson method. The Cate-Nelson model indicates that for yields above 6974.9 kg/ha, the CNDr2 index value must be below 17.645. According to clr indices the most critical leaf nutritional indices influencing the performance of low-yield ‘Sirvan’ wheat fields on calcareous and saline soils were identified. Among the leaf nutrient indices in low-yield populations of ‘Sirvan’ wheat fields, increasing leaf Ca, P, Mn, Cu, N, and Zn, while decreasing Mg, B, and K, and bringing them closer to zero within the range of nutritional balance, can have significant effects on yield under saline and calcareous soil conditions, countering the negative impact of high pH due to alkaline salt stress.

Harnessing UAVs and deep learning for accurate grass weed detection in wheat fields: a study on biomass and yield implications

Weeds are undesired plants competing with crops for light, nutrients, and water, negatively impacting crop growth. Identifying weeds in wheat fields accurately is important for precise pesticide spraying and targeted weed control. Grass weeds in their early growth stages look very similar to wheat seedlings, making them difficult to identify. In this study, we focused on wheat fields with varying levels of grass weed infestation and used unmanned aerial vehicles (UAVs) to obtain images. By utilizing deep learning algorithms and spectral analysis technology, the weeds were identified and extracted accurately from wheat fields. Our results showed that the precision of weed detection in scattered wheat fields was 91.27% and 87.51% in drilled wheat fields. Compared to areas without weeds, the increase in weed density led to a decrease in wheat biomass, with the maximum biomass decreasing by 71%. The effect of weed density on yield was similar, with the maximum yield decreasing by 4320 kg·ha− 1, a drop of 60%. In this study, a method for monitoring weed occurrence in wheat fields was established, and the effects of weeds on wheat growth in different growth periods and weed densities were studied by accurately extracting weeds from wheat fields. The results can provide a reference for weed control and hazard assessment research.

An overview of the role of nanoherbicides in tackling challenges of weed management in wheat: A novel approach

Abstract One of the most significant biotic constraints that wheat production faces is weed infestation. Wheat is infested with different weeds that cause yield losses (up to 100%) that vary based on the type of weed, their density, and the environmental conditions. Chemical weed control is the most common method to control weeds in wheat. However, widespread herbicide resistance (>365 cases worldwide) has challenged the sustainability of this method. Use of nanoherbicides is a promising strategy to cope with the issue of herbicide resistance. To achieve weed control conditions during the whole growing season, nanoformulations of herbicides are a delivery strategy that involves covering an active component with various materials that vary in size from nano to nanoscale and then releasing the substance in a controlled manner. Nanoherbicides prevent leaching and volatilization of active components and premature degradation through photolysis, hydrolysis, and biodegradation. According to studies, nanoencapsulation of herbicides produces more targeted and less hazardous agricultural formulations. Using nanoherbicides in lower concentrations is beneficial. It lessens the long-term impacts of herbicide residues in wheat fields and the toxicity of these herbicides to the environment. It is also beneficial in eliminating the weeds without ever interacting with the crop plants, which eventually results in a greater wheat yield. This review provides a comprehensive overview of the emerging field of utilizing nanoparticles (NPs) in herbicides for effective weed management in wheat crops. This article explores the novel approach of integrating NPs with herbicidal agents, highlighting their potential benefits and challenges. The review also addresses the current state of research, recent advancements, and potential future directions in this evolving area of agricultural science.

Soil bacterial communities are influenced by mulching methods and growth stages in dryland wheat fields

Soil bacterial communities are influenced by mulching methods and growth stages in dryland wheat fields

Drought impairs detritivore feeding activity more strongly in northern than in southern European latitudes

Soil detritivores play key roles in decomposition processes closely related to ecosystem services. Drought and soil organic carbon depletion due to agricultural management are detrimental to soil biodiversity, but their interactive effects on soil biota and associated processes have not been thoroughly investigated. In 2018, we used rain-out shelters to experimentally induce drought in wheat fields of contrasting levels of organic carbon in Sweden, Germany and Spain. That year Europe experienced a climatic dipole, with exceptionally severe droughts in northern latitudes. We assessed the feeding activity of soil detritivores by bait-lamina tests, and measured several abiotic and biotic soil parameters. In the peak of the dipole drought (summer) southern fields had the driest soils. Nonetheless, detritivore feeding activity responded to the experimental drought by shifting to deeper soil layers there. Low soil organic carbon (SOC) levels exacerbated the latter effect. However, in this same period, feeding activity in northern and central Europe was two orders of magnitude lower than in the south, and failed to respond to either the experimental treatment and/or SOC levels. Using mixed-effects longitudinal random forests, we detected various candidate drivers of detritivore feeding activity: soil moisture, phosphorus content, bacteria and nematodes. Different bacterial taxa were associated to detritivory in each country, but their potential influence was pervasive. Thus, our results suggest that drought had adverse effects on detritivore feeding, which were exacerbated northwards due to the climatic dipole. Increased SOC levels mitigated drought effects only in southern soils. Regional adaptation of soil biota to aridity could explain the response of detritivores in southern Europe to drought. Machine learning algorithms arise as useful tools for exploring potential drivers relating biodiversity to soil processes. Overall, future research on the effects of drought on soil biodiversity and processes will be key for tackling climate change impacts in terrestrial ecosystems.

Prediction of Soil Temperature in Wheat Field Using Machine Learning Models

ABSTRACT Wheat is a very vital cereal crop in the development of a nation’s economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin’s concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively.

Assessment of the effects of winter wheat scattering on SAR backscatter for soil moisture estimation based on a radiative transfer model

ABSTRACT This study evaluated the potential of the Single Scattering Radiative Transfer (SSRT) model coupled with the Oh model to retrieve surface soil moisture (SSM) in two drip-irrigated wheat fields in the Tensift al Haouz plain, over three growing seasons (2016–2019). This research focuses on evaluating isotropic scattering and Rayleigh scattering by calibrating the coupled SSRT_Oh model, using data gathered on the calibrated field. The data includes measured SSM at 5 cm depth, Leaf Area Index (LAI) and vegetation height (H). The aim is to fit the extinction coefficient through comparing simulated and Sentinel-1 backscatter, at VV and VH polarizations. The validated field data were used to retrieve SSM. Calibration revealed the best retrieval for Rayleigh scattering, at VV polarization, with an RMSE of 1.25 dB, a correlation coefficient (r) of 0.86, and a bias of 0.1 dB, whereas, the isotropic approach yielded r, RMSE and bias values of 0.83, 1.37 dB and −0.01 dB, respectively. Rayleigh’s performance remained unchanged throughout the inversion process for SSM retrieval, at VV polarization, with r of 0.87 against 0.86 for isotropic scattering. To enhance our findings, the introduction of dynamic extinction parameter and exclusive use of Sentinel-1 data to describe vegetation could improve SSM retrieval.

Subfield-level crop yield mapping without ground truth data: A scale transfer framework

Ongoing advances in satellite remote sensing data and machine learning methods have enabled crop yield estimation at various spatial and temporal resolutions. While yield mapping at broader scales (e.g., state or county level) has become common, mapping at finer scales (e.g., field or subfield) has been limited by the lack of ground truth data for model training and evaluation. Here we present a scale transfer framework, named Quantile loss Domain Adversarial Neural Networks (QDANN), that leverages knowledge from county-level datasets to map crop yields at the subfield level. Based on the strategy of unsupervised domain adaptation, QDANN is trained on labeled county-level data and unlabeled subfield-level data, with no requirement for yield information at the subfield level. We evaluate the proposed method applied to Landsat imagery and Gridmet weather data for maize, soybean, and winter wheat fields in the United States, using as reference data yield monitor records from roughly one million field-year observations. The model is compared with several process-based and machine learning-based benchmark approaches that train on simulated yield records or county-level data. QDANN-estimated yields achieved an R2 score (RMSE) of 48 % (2.29 t/ha), 32 % (0.85 t/ha), and 39 % (1.40 t/ha) for maize, soybean, and winter wheat in comparison with the ground-based yield measures, respectively. These performances are higher than benchmark approaches and are nearly as good as models trained on field-level data. When aggregated to the county level, the improvement achieved by QDANN is more pronounced and the R2 scores (RMSE) improved to 78 % (0.98 t/ha), 62 % (0.37 t/ha), and 53 % (1.00 t/ha) for maize, soybean, and winter wheat, respectively. This study demonstrates that the proposed scale transfer framework can serve as a reliable approach for yield mapping at the subfield level when there is no access to fine-scale yield information. Based on the QDANN model, we have generated and made publicly available 30-m annual yield maps for major crop-producing states in the U.S. since 2008.

Deep learning for image-based detection of weeds from emergence to maturity in wheat fields

Effective weed control in wheat (Triticum aestivum L.) fields is crucial for optimizing production and ensuring food security in semi-arid regions. The implementation of deep learning for weed detection could enable precise weed management, leading to enhanced wheat yield, increased income for growers through targeted herbicide use, and a reduced environmental footprint associated with herbicide application. Charlock mustard (Sinapis arvensis L.) [CM], creeping thistle (Cirsium arvense (L.) Scop) [CT], and forking larkspur (Consolida regalis Gray) [FL]) are among the most invasive weed species in wheat fields. Effective management of these weeds is essential to ensure optimal wheat productivity in the semi-arid regions. To detect weeds, an unmanned arial vehicle was used to collect images and videos from 185 wheat fields in Tokat, Turkey. The images were collected from five phenological periods of each species including seedling, rosette, vegetative, flowering, and fruiting (in total 15 classification process). These stages are either important for timely weed control or essential for understanding weed pressure threshold, the potential for increasing weed seedbank increase, and identifying herbicide resistant weeds. A total of 145,792 objects were labeled in the images to create the dataset, divided into 80 % for training + validation and 20 % for testing. The dataset was used with the YOLOv5 (You Only Look Once) deep learning architecture. The YOLOv5 was the newest YOLO model during this study carried out. All neural networks provided by YOLOv5 (nano, small, medium, large, and xlarge) were trained and the Precision, Recall, F-1 Score, and AUC (Area under the Curve) performances of the neural networks were evaluated. The YOLOv5s (small) was the most precise neural network in detecting weeds regardless of the species. In contrast, nano was the least precise neural network in detecting each weed species. For CT, when small neural network was used, precision of detection ranged from 0.87 (fruit stage) to 0.96 (rosette and vegetative stages). For, forking larkspur, the best neural network was achieved with YOLOv5 small and the precision ranged from 0.45 (fruit stage) to 0.93 (vegetative stage). Using YOLOv5 small in Charlock mustard, the most precise detection occurred at seedling, rosette, and vegetative stages (with precision of 0.96) and the least precise detection was recorded at the fruit stage (0.81). Other performance evaluations (Recall, F-1 Score, and AUC) were in agreement with those of Precision. Our results indicated that by using YOLOv5 small neural network, all three weed species could be detected at all growth stages with the precision of 0.86 with the exception of fruit stage. Future research should (1) focus on assessing YOLOv5 small neural network with the newest versions of YOLO and also (2) evaluate this neural network for detecting weed species in wheat fields to allow for improved weed control and future recommendations.

Reducing Nitrogen Application Rates and Straw Mulching Can Alleviate Greenhouse Gas Emissions from Wheat Field Soil and Improve Soil Quality

Reducing Nitrogen Application Rates and Straw Mulching Can Alleviate Greenhouse Gas Emissions from Wheat Field Soil and Improve Soil Quality

Phoma herbarum: A Potential Biocontrol Agent Against Weeds, that Promotes Wheat Growth

The usage of chemical weedicide adversely affects the soil fertility and environment. Hence, in order to reduce the use of chemical weedicide, current study was aimed to isolate plant pathogenic microorganisms from diseased weeds and evaluate their potential as a bioherbicide in wheat field. Twelve bacterial and thirty-one fungal isolates were screened to determine their bioherbicidal activity against prevalent weeds (Avena fatua, Phalaris minor, and Chenopodium album) by using detached leaf assay and in-vitro seed testing methods. Among the forty-three isolates, two potential isolates were selected for further studies. Potential fungal isolates DGL 8C and DGL 7A with significant bioherbicidal activity were molecularly (ITS sequencing) identified as Phoma herbarum R21 (GenBank ID- ON705696) and K_NESO2 (GenBank ID- ON705704). Phoma herbarum R21 was chosen for further research due to its superior herbicidal effect and positive influence on wheat growth. Effective herbicidal activity (up to 90%) of potential isolate was obtained in pre-germination testing, compared to control. Cell free culture filtrate (CFCF) treatment showed nonspecific inhibition in the germination of weeds and wheat. While, CFCF selectively deteriorated the target weeds in post-germination treatment. Phoma herbarum R21 enhanced the growth of Durum wheat varieties Poshan and Tejas, as it promoted the growth of shoot, root, and fresh weight up to 88% compared to control. Phoma herbarum R21 significantly inhibited the growth of phytopathogenic fungi up to 57%. In this study, Phoma herbarum R21 was identified as a potential bioherbicide against the weeds of wheat along with its growth promoting and antifungal activities.

The herbicidal activity of pre-emergence herbicide cinmethylin and its potential risks on soil ecology: pH, enzyme activities and bacterial community

BackgroundThe herbicide cinmethylin, which was originally registered for use in rice fields, has the potential to control grass weeds in wheat fields before the emergence of wheat. However, its herbicidal activity against various troublesome grass weeds that infest wheat fields in China and its relationships with soil pH, soil enzymes and soil bacteria are not well known. Here, the effects of applying cinmethylin on the soil surface were tested on six grass weeds, and its impacts on soil characteristics, including the soil pH, soil enzymes and bacterial community, were evaluated.ResultsAlopecurus aequalis, A. japonicus and A. myosuroides were highly sensitive to cinmethylin, with GR50 values of 78.77, 61.49 and 119.67 g a.i. ha− 1, respectively. The half-lives of cinmethylin at 1-, 10- and 100-fold the recommended rates were estimated at 26.46 − 52.33 d. Cinmethylin significantly increased the soil pH but decreased the activities of soil sucrase and urease. At 10- and 100-fold the recommended rate of cinmethylin, the bacterial abundance and diversity significantly decreased at 30 and 60 days after cinmethylin treatment. Cinmethylin at 100-fold the recommended rates largely promoted bacterial co-occurrence network complexity. Cinmethylin at high concentrations temporarily inhibited the abundance of the Nitrospira genus, as indicated by the copy numbers of the ammonia-oxidising archaea (AOA) amoA and ammonia-oxidising bacteria (AOB) amoA genes. Further analysis revealed that soil pH was negatively related to soil urease, and a significantly positive correlation was detected between soil urease and soil nitrification.ConclusionCollectively, the application of cinmethylin at the recommended field dose had nearly no effect on the soil ecosystem, but its potential risks at high concentrations deserve further attention.

Deciphering the differences of bacterial communities between high- and low-productive wheat fields using high-throughput sequencing.

Microbial communities have been demonstrated to be essential for healthy and productive soil ecosystems. However, an understanding of the relationship between soil microbial community and soil productivity levels is remarkably limited. In this study, bulk soil (BS), rhizosphere soil (RS), and root (R) samples from the historical high-productive (H) and low-productive (L) soil types of wheat in Hebei province of China were collected and analyzed by high-throughput sequencing. The study highlighted the richness, diversity, and structure of bacterial communities, along with the correlation networks among different bacterial genera. Significant differences in the bacterial community structure between samples of different soil types were observed. Compared with the low-productive soil type, the bacterial communities of samples from the high-productive soil type possessed high species richness, low species diversity, complex and stable networks, and a higher relative abundance of beneficial microbes, such as Pseudoxanthomonas, unclassified Vicinamibacteraceae, Lysobacter, Massilia, Pseudomonas, and Bacillus. Further analysis indicated that the differences were mainly driven by soil organic matter (SOM), available nitrogen (AN), and electrical conductivity (EC). Overall, the soil bacterial community is an important factor affecting soil health and crop production, which provides a theoretical basis for the targeted regulation of microbes in low-productivity soil types.

Resistance to acetyl-CoA carboxylase (ACCase)-inhibiting herbicides in Lolium multiflorum Lam. populations of Argentina.

Italian ryegrass (Lolium multiflorum Lam.) is one of the most troublesome grass weeds in Argentina. The extensive and repetitive use of acetyl-CoA carboxylase (ACCase)-inhibiting herbicides has induced resistance in this weed species. The objectives of this study were to quantify the resistance levels to ACCase-inhibiting herbicides in two resistant populations and to identify the target-site mutations associated with their resistance. Two resistant Italian ryegrass populations, Roldán and H2, were studied. Roldán was a suspected haloxyfop-resistant population, located in a wheat field from Santa Fe province with a history of ACCase-inhibiting herbicide use. The H2 population was obtained from the susceptible Hernandarias population (H0) after two cycles of selection with the herbicide quizalofop-ethyl. Whole-plant dose-response assays revealed that the resistant populations exhibited a high resistance to haloxyfop, with resistance factors (RF) exceeding 97-fold. Additionally, both populations showed a moderate resistance to pinoxaden (RF > 7), while maintaining susceptibility to clethodim. Partial chloroplastic ACCase sequences revealed isoleucine-to-asparagine substitution at position 2041 (Ile-2041-Asn) in both resistant populations. This work provides a better understanding of cross-resistance to ACCase-inhibiting herbicides in L. multiflorum populations and represents the first report of the target-site mutation Ile-2041-Asn conferring resistance in populations from Argentina. © 2024 Society of Chemical Industry.

High soil moisture promotes the emergence of ground beetles and spiders from soils in wheat fields

Promoting arthropods in agricultural landscapes can contribute substantially to stop their decline and enhance pest control. Higher soil moisture and the presence of field margins can increase the abundance of arthropods in agricultural landscapes and influence their distribution within crop fields. However, little is known about the influence of soil moisture and distance from field margins on the overwintering of arthropods in arable fields. We investigated the influence of soil moisture and distance from a field margin on the numbers of arthropods, ground beetles and spiders emerging from soil in winter wheat fields. We established transects in winter wheat fields away from two different types of field margins: (i) around small standing water bodies (kettle holes) to capture a wide range of soil moisture values and (ii) other semi-natural landscape elements. At three distances (1 m, 20 m, 50 m), we sampled arthropods with emergence traps and measured soil moisture between March and June. We found that soil moisture had a positive effect on the emergence numbers of arthropods in general and ground beetles and spiders in particular. Distance from field margins generally had negative effects on the emergence numbers of ground beetles, but positive effects on the emergence numbers of spiders. Emergence numbers and soil moisture content did not differ significantly between the two types of field margins. The high emergence numbers inside the fields indicate that arable fields are important overwintering habitats for beneficial arthropods. Proper management of arable soils to promote soil water holding capacity and soil moisture content may have the added benefit of promoting the production of beneficial natural enemies from local soils.

An ethnobotanical study of commonly growing weeds in wheat fields of District Chitral Lower, Pakistan

An ethnobotanical study of commonly growing weeds in wheat fields of District Chitral Lower, Pakistan

Registration of six albinism wheat sib lines for genetic aberration of photosynthetic pigments

AbstractPlant biologists have long been fascinated with the abnormal, the monstrous, and the defective. Six sib winter wheat (Triticum aestivum L.) lines with varying types of albinism, ShunMai GAG‐1 (Reg. no. GP‐1090, PI 704106), ShunMai GAG‐2 (Reg. no. GP‐1091, PI 704107), ShunMai GAG‐3 (Reg. no. GP‐1092, PI 704108), ShunMai GAG‐4 (Reg. no. GP‐1093, PI 704109), ShunMai GAG‐5 (Reg. no. GP‐1094, PI 704110), and ShunMai GAG‐6, (Reg. no. GP‐1095, PI 704111), were derived from a cross made in 2013 with unknown pedigree, and were developed using conventional phenotypic selections. Albinism, or stage‐specific albino, is their unique abnormal agronomic characteristics. All their leaves and tillers produced before winter are green, all their spring‐emerging leaves and tillers are albino, and all their summer‐producing leaves turn green again. Their main stems could have at least three albino leaves. In some colder springs, their after‐winter emerging leaves are reddish to pinkish. They may serve as useful experimental materials for addressing a wide range of wheat breeding problems, and for wheat field art too, and may also be important materials for functional studies and eventually lead to the gene discovery. We discuss the possible interactions between plastid genes and nuclear genes and between vernalization genes responsible for the transition from vegetative to generative growth stage and photosynthetic genes.

Modeling the impact of long-term land use changes on deep soil hydrological processes in the Loess Plateau, China

Land use change can significantly affect soil hydrology in arid and semi-arid regions, making it crucial to understand the relationship between vegetation roots and soil moisture. Current models often fail to predict root growth and its impacts on water dynamics accurately. Our work presents a novel model that seamlessly integrates the Community Land Model (CLM) with the Soil & Water Assessment Tool (SWAT). Furthermore, it enhances the root module within the CLM, enabling more accurate simulations of dynamic root depth and distribution across varying tree ages. This improvement particularly considers the crucial processes of dormancy and plant maturity. Soil moisture and root patterns under apple trees of varying ages and in wheat fields on the Loess Plateau was analyzed. Our findings indicate that our dynamic root depth model outperforms traditional static models, and can accurately reflect soil moisture levels with high precision (R2 = 0.80–0.81; Nash-Sutcliffe efficiency (NSE) = 0.65–0.75). In contrast to methods that utilize fixed root depths, dynamic root simulation can provide new insights. As apple orchards mature, the roots of 22-year-old apple trees have been found to reach a depth of 21 m in the soil. Conversely, the maximum root depth of wheat is limited to 1.9 m. This latter finding aligns more closely with the measured root depths, highlighting the accuracy of dynamic simulations. This model reveals that older apple orchards show decreased soil moisture at greater depths (>20 m), contrasting with wheat fields that affect moisture mostly within the top 2 m. Our results underscore the crucial role of dynamic modeling in comprehending root-soil water interactions. Furthermore, they imply that extended orchard cultivation practices can lead to a substantial depletion of deep soil moisture. Specifically, over a period of 1 to 22 years, a water deficit of up to 85 mm yr−1 has been observed. For a 22-year-old forest, the D-D (dynamic distributions of coarse and fine roots) method calculates a significant cumulative deep Soil Water Storage loss. Over the course of 22 years, this loss amounts to 1664 mm, which is almost three times compare to the annual rainfall recorded. Such a large loss has the potential to significant impact on groundwater recharge. This highlights the need for careful consideration in future afforestation efforts to prevent increased soil aridity.

Herbicidal and Nitrogen Efficacy on Weed Management in Wheat Fields of Eastern Uttar Pradesh, India

Weeds can significantly affect crop yield and nutrient uptake, making effective management crucial in wheat fields. This study, conducted during the winter (Rabi) season of 2018-19 at the Agricultural Research Farm of Banaras Hindu University in Varanasi, aimed to assess the combined impact of different nitrogen levels and herbicide treatments on weed control and wheat production. The objective was to determine the best practices for reducing weed competition and improving crop performance. The experiment was set up using a split plot design with three replications. The treatments comprised of 3 nitrogen levels and 5 weed control methods. The study identified nine prevalent weed species in wheat fields, including Phalaris minor, Anagallis arvensis, Cynodon dactylon, Chenopodium album, Melilotus indicus, Vicia sativa, Medicago denticulata, Solanum nigrum, and Cyperus rotundus, with Anagallis arvensis, Chenopodium album and Vicia sativa being the most dominant. The application of Sulfosulfuron (25 g ha-1) combined with 2, 4-D (750 ml ha-1) resulted in the lowest weed density and biomass and the highest weed control efficiency. Additionally, performing hand weeding twice (30 and 60 days after sowing) in conjunction with 180 kg N ha-1, followed by the application of Sulfosulfuron (25 g ha-1) + 2, 4-D (750 ml ha-1), significantly reduced Anagallis arvensis, Chenopodium album and Vicia sativa population and biomass and improved weed control effectiveness. Higher weed dry weight and population results in lower plant nutrient uptake and lower dry matter of crop plant and yield.