Varying Growth Stages Research Articles

Identification of weeds in cotton fields at various growth stages using color feature techniques

Weeds affect the growth and yield of cotton (Gossypium hirsutum), which is the most prevalent commercially viable non-food crop. Accurate differentiation between weeds and cotton plants is crucial for implementing precise weed-control strategies, optimizing crop yields, and minimizing herbicide usage. In this study, we aimed to investigate the effectiveness of using color feature techniques combined with Otsu's method (OTSU) to distinguish weeds from cotton plants at various growth stages. The results revealed that the image segmentation approach achieved a recognition rate of 74.17% during the second growth stage. In the third growth stage, the weed identification process primarily targeted lambsquarters (Chenopodium album). The standard deviation (SD) difference between the red (R), green (G), and blue (B) components of the plant images revealed that the difference between the SD values of R and B was <5, which could serve as an effective threshold for identifying lambsquarters. Additionally, the recognition rates for cotton and lambsquarters were 71.40% and 92.90%, respectively, and the overall recognition rate reached 82.10% during the identification process. The results of this study have practical implications for weed management practices, such as targeted herbicide application, mitigation of environmental impacts, and contribution to higher crop yields. Future research will focus on refining these methods by exploring advanced image processing techniques that integrate deep learning for automatic feature extraction, which will be evaluated for handling complex agronomic scenarios, such as leaf occlusion, varying growth stages, and image noise.

Efficient crop segmentation net and novel weed detection method

Computer vision-based precision weed control offers a promising avenue for reducing herbicide input and the associated costs of weed management. However, the substantial investments in time and labor required for the collection and annotation of weed image data pose challenges to develop effective deep learning models. The limitation also stems from the challenges in achieving accurate and reliable detection of weeds across varying growth stages, densities, and ecotypes in field scenarios. To address these issues, this research investigated a novel methodology employing a segmentation algorithm to accurately mark the contour information of crops in the image and detect weeds through image processing technology. Furthermore, a novel segmentation network was developed based on the YOLO architecture to address the substantial computing resource demands associated with segmentation algorithms. This was achieved through the design of a new backbone, incorporation of an attention mechanism, and modification of the feature fusion technique. The novel network achieved higher segmentation accuracy with less computational demands. The effectiveness of three different attention modules on segmentation tasks was additionally investigated. Experimental results showed that the insertion of Criss-cross Attention significantly improved the model's performance and was subsequently incorporated into our enhanced methodology. The enhanced model achieved a Mean Intersection over Union (mIoU50) of 90.9 %, with precision increasing by 5.9 % and Giga FLoating-point Operations Per Second (GFLOPs) reduced by 15.56 %, demonstrating enhanced suitability for deployment in resource-constrained computing environments. The findings presented in this study hold substantial theoretical and practical implications for precise weed management.

Reparation with moving least squares sampling and extraction of body sizes of beef cattle from unilateral point clouds

Body measurements of beef cattle at various growth stages are crucial for assessing breeding quality. To decrease the stress responses of live cattle during measurement, noncontact body size measurement using a deep sensor is conducted. After directly obtaining the point cloud data (PCD) of a unilateral surface, novel point cloud repair and body measurement are proposed. The PCD of beef cattle are first preprocessed using Euclidean clustering, conditional filtering, and Random Sample Consensus. To merge the unilateral PCD into the entire contour, a Euclidean transformation matrix and moving least squares are applied. To address holes caused by stitching due to camera angle restrictions, various upsampling techniques based on least squares deformation are investigated to repair holes in different surfaces with varying growth stages. Finally, polynomial fitting is chosen to be in accordance with the feature point distribution of the body height (BH), chest girth (CG), back height (AH), waist height (WH), ischial tuberosity, and shoulder end of beef cattle. Using the formulas of Euclidean distance and cubic B–spline curve fitting, the values of BH, AH, WH, CG, and body length are obtained and then compared with manual measurement data. The predicted values of the five body sizes of 100 beef cattle had a mean absolute error and mean absolute percentage error of 7.77 cm and 5.42 %, respectively. The largest absolute error was 12.05 cm, and the smallest absolute error was 4.12 cm. A series of PCD processing and repair methods provides a new method for the noncontact automated measurement of beef cattle. This approach promotes more effective practices in livestock welfare and trait assessment compared with traditional management.

Spectral mixture analysis for weed traits identification under varying resolutions and growth stages

Spectral and spatial resolutions are critical factors in Site-Specific Weed Management (SSWM) applications, which aim to adjust and apply herbicides according to the weed composition and coverage during early growth. Thus, a reliable SSWM requires high spectral and spatial resolution data to create informative maps indicating and spatially localizing the weed species. Besides, upscaling and improving the mapping process are essential to motivate farmers to adopt SSWM practices. However, high-resolution data in spectral and spatial domains is not always available, and mixed pixels are highly probable. Therefore, evaluating approaches that deal with such pixels that pose a significant problem for weed classification tasks is vital. In this regard, Spectral Mixture Analysis (SMA) can exploit subpixel information from coarse-resolution data. However, despite its potential, SMA has not been examined for subpixel weed mapping. In this work, we aim to evaluate the potential advantages of SMA for coverage estimation of different weed traits (weed botanical group, photosynthetic pathway, and species). For this purpose, we first created a dataset containing weed species characterized by different botanical groups and photosynthetic pathways. Then, we evaluated the performance of SMA in estimating the weed’s coverage at varying growth stages and spectral and spatial resolutions. The experimental results revealed that using botanical weed groups as Endmembers (EM) is advantageous over other traits. A quantitative evaluation revealed strong correlation values between the actual and predicted weed coverage and a Mean Absolute Error (MAE) lower than 10 % for the different EM at all resolutions and growth stages. Besides, SMA outperformed the traditional classification-based approach in estimating weed coverage with up to ∼ 30 % lower Mean Absolute Error. Finally, our study establishes a basis for incorporating SMA in various SSWM practices.

HSDNet: a poultry farming model based on few-shot semantic segmentation addressing non-smooth and unbalanced convergence.

Poultry farming is an indispensable part of global agriculture, playing a crucial role in food safety and economic development. Managing and preventing diseases is a vital task in the poultry industry, where semantic segmentation technology can significantly enhance the efficiency of traditional manual monitoring methods. Furthermore, traditional semantic segmentation has achieved excellent results on extensively manually annotated datasets, facilitating real-time monitoring of poultry. Nonetheless, the model encounters limitations when exposed to new environments, diverse breeding varieties, or varying growth stages within the same species, necessitating extensive data retraining. Overreliance on large datasets results in higher costs for manual annotations and deployment delays, thus hindering practical applicability. To address this issue, our study introduces HSDNet, an innovative semantic segmentation model based on few-shot learning, for monitoring poultry farms. The HSDNet model adeptly adjusts to new settings or species with a single image input while maintaining substantial accuracy. In the specific context of poultry breeding, characterized by small congregating animals and the inherent complexities of agricultural environments, issues of non-smooth losses arise, potentially compromising accuracy. HSDNet incorporates a Sharpness-Aware Minimization (SAM) strategy to counteract these challenges. Furthermore, by considering the effects of imbalanced loss on convergence, HSDNet mitigates the overfitting issue induced by few-shot learning. Empirical findings underscore HSDNet's proficiency in poultry breeding settings, exhibiting a significant 72.89% semantic segmentation accuracy on single images, which is higher than SOTA's 68.85%.

Higher vegetation sensitivity to meteorological drought in autumn than spring across European biomes

Europe has experienced severe drought events in recent decades, posing challenges to understand vegetation responses due to diverse vegetation distribution, varying growth stages, different drought characteristics, and concurrent hydroclimatic factors. To analyze vegetation response to meteorological drought, we employed multiple vegetation indicators across European biomes. Our findings reveal that vegetation sensitivity to drought increases as the canopy develops throughout the year, with sensitivities from −0.01 in spring to 0.28 in autumn and drought-susceptible areas from 18.5 to 57.8% in Europe. Soil water shortage exacerbates vegetation-drought sensitivity temporally, while its spatial impact is limited. Vegetation-drought sensitivity strongly correlates with vapor pressure deficit and partially with atmospheric CO2 concentration. These results highlight the spatiotemporal variations in vegetation-drought sensitivities and the influence of hydroclimatic factors. The findings enhance our understanding of vegetation response to drought and the impact of concurrent hydroclimatic factors, providing valuable sub-seasonal information for water management and drought preparedness.

Hyperspectral imaging with chemometrics for non-destructive determination of cannabinoids in floral and leaf materials of industrial hemp (Cannabis sativa L.)

With the passage of the 2018 Farm Bill, industrial hemp (Cannabis sativa L.) has become a legal and economically promising crop commodity for U.S. farmers. There has been a surge of interest in growing industrial hemp for producing cannabinoids, such as cannabidiol (CBD), because of their medical potential. Quantitative determination of cannabinoids in harvested materials (primarily floral tissues) is critical for cannabinoid production and compliance testing. The concentrations of cannabinoids in hemp materials are conventionally determined using wet-chemistry chromatographic methods, which require destructive sampling, and are time-consuming, costly, and thus not suitable for on-site rapid testing. This study presents a novel effort to utilize hyperspectral imaging technology for non-destructive quantification of major cannabinoids, including CBD, THC (tetrahydrocannabinol), CBG (cannabigerol) and their acid forms in fresh floral and leaf materials of industrial hemp on a dry weight basis. Hyperspectral images in the wavelength range of 400–1000 nm were acquired from floral and leaf tissues immediately after harvest from a total of 100 industrial hemp plants of five cultivars at varied growth stages. Linear discriminant analysis showed hyperspectral imaging could identify CBD-rich/poor and THC-legal/illegal flower samples with accuracies of 99% and 97%, respectively. Quantitative models based on full-spectrum PLS (partial least squares) achieved prediction accuracies of RPD (ratio of prediction to deviation) = 2.5 (corresponding R2 = 0.84) for CBD and THC in floral tissues. Similar accuracies were obtained for their acid forms in flower samples. The predictions for CBG and its acid form in floral tissues and all six cannabinoids in leaf tissues were unsatisfactory with noticeably lower RPD values. Consistently improved accuracies were obtained by parsimonious PLS models based on a wavelength selection procedure for minimized variable collinearity. The best RPD values of approximately 2.6 (corresponding R2 = 0.85) were obtained for CBD and THC in floral materials. This study demonstrates the utility of hyperspectral imaging as a potential valuable tool for rapid quantification of cannabinoids in industrial hemp.

Bacillus velezensis strain B26 modulates the inflorescence and root architecture of Brachypodium distachyon via hormone homeostasis

Plant growth-promoting rhizobacteria (PGPR) influence plant health. However, the genotypic variations in host organisms affect their response to PGPR. To understand the genotypic effect, we screened four diverse B. distachyon genotypes at varying growth stages for their ability to be colonized by B. velezensis strain B26. We reasoned that B26 may have an impact on the phenological growth stages of B. distachyon genotypes. Phenotypic data suggested the role of B26 in increasing the number of awns and root weight in wild type genotypes and overexpressing transgenic lines. Thus, we characterized the expression patterns of flowering pathway genes in inoculated plants and found that strain B26 modulates the transcript abundance of flowering genes. An increased root volume of inoculated plants was estimated by CT-scanning which suggests the role of B26 in altering the root architecture. B26 also modulated plant hormone homeostasis. A differential response was observed in the transcript abundance of auxin and gibberellins biosynthesis genes in inoculated roots. Our results reveal that B. distachyon plant genotype is an essential determinant of whether a PGPR provides benefit or harm to the host and shed new insight into the involvement of B. velezensis in the expression of flowering genes.

Integrated Analysis of Osmotic Stress and Infrared Thermal Imaging for the Selection of Resilient Rice Under Water Scarcity.

The climate change scenario has increased the severity and frequency of drought stress, which limits the growth and yield of rice worldwide. There is a dire need to select drought-tolerant rice varieties to sustain crop production under water scarcity. Therefore, the present study effectively combined morpho-physiological and biochemical approaches with the technology of infrared thermal imaging (IRTI) for a reliable selection of drought-tolerant genotypes. Initially, we studied 28 rice genotypes including 26 advance lines and three varieties for water stress tolerance under net house conditions. Three genotypes NIBGE-DT-02, KSK-133, and NIBGE-DT-11 were selected based on the Standard Evaluation System (SES) scoring for drought tolerance. NIBGE-DT-02 showed tolerance to polyethylene glycol (20%) induced osmotic stress indicated by a minimum reduction in seedling length, biomass, chlorophyll content, and increased leaf proline content as compared to susceptible varieties under a hydroponic system. NIBGE-DT-02 was further evaluated for water withholding at varying growth stages, i.e., 30 and 60 days after transplantation (DAT) in pots under net house conditions. NIBGE-DT-02 showed a significantly lower reduction (35.9%) in yield as compared to a susceptible variety (78.06%) under water stress at 60 DAT with concomitant induction of antioxidant enzymes such as peroxidase, catalase, and polyphenol oxidase. A significant increase (45.9%) in proline content, a low increase (7.5%) in plant temperature, along with a low reduction in relative water content (RWC) (5.5%), and membrane stability index (MSI) (9%) were observed under water stress at 60 DAT as compared to the well-watered control. Pearson correlation analysis showed the strong correlation of shoot length with MSI and root length with RWC in rice genotypes at the later growth stage. Furthermore, Regression analysis indicated a negative correlation between plant temperature of NIBGE-DT-02 and proline, RWC, MSI, and peroxidase enzyme under variable water stress conditions. All these responses collectively validated the adaptive response of selected genotypes under water stress during different growth stages. Tolerant genotypes can be used in breeding programs aimed at improving drought tolerance and can expand rice cultivation. Furthermore, this study provides a foundation for future research directed to utilize IRTI as a fast and non-destructive approach for the selection of potent rice genotypes better adapted to water scarcity from wide germplasm collection.

PROSDM: Applicability of PROSPECT model coupled with spectral derivatives and similarity metrics to retrieve leaf biochemical traits from bidirectional reflectance

The PROSPECT model, one of the most popular leaf radiative transfer models, has long been used for retrieving leaf biochemical traits from leaf directional-hemispherical reflectance factor (DHRF) spectra, while remains underexplored for applications to the leaf bidirectional reflectance factor (BRF) spectra to retrieve leaf biochemical traits. Existence of leaf surface reflectance and anisotropic property could be the main issues that constrain the applicability of the PROSPECT inversion to assess leaf biochemical traits from leaf BRF spectra. This study presents an inversion method by integrating the PROSPECT model with spectral derivatives and similarity metrics (SDM), called PROSDM, to remove the difference between leaf BRF and DHRF spectra and retrieve leaf biochemical traits from leaf BRF spectra. Leaf BRF spectra and the corresponding contents of chlorophyll (Cab), carotenoid (Cxc), water (Cw), and dry matter (Cm) were obtained from ten datasets across a wide variety of plant species with varied growth stages, nutrition status, and planting regions. The datasets showed that the significant difference existed between leaf BRF and DHRF spectra, which varied with spectral wavelengths and plant species, and affected the accuracies of retrieving Cab, Cxc, Cw, and Cm. To eliminate the difference between leaf BRF and DHRF spectra independent on wavelengths, spectral derivatives were applied. When the difference between BRF and DHRF spectra varied with wavelengths, spectral derivatives only removed part of the difference, and implementation of the Manhattan distance compensated the limitation of the spectral derivatives to further reduce the difference. As a result, the PROSDM outperformed the PROSPECT and PROCOSINE inversions as well as PROCWT to retrieve Cab, Cxc, Cw, and Cm from leaf BRF spectra with the root mean square errors (RMSEs) of 7.64 μg/cm2, 2.77 μg/cm2, 0.0041 g/cm2, and 0.0024 g/cm2, respectively. Significant improvements in the retrievals of Cab, Cxc, Cw, and Cm were obtained with the RMSEs reduced by 20.33%, 29.34%, 25.45%, and 44.19% compared to the PROSPECT inversion, respectively. It was further found that the model inversions were affected by the spectral saturation, PROSPECT versions, spectral subdomains, and the range of model parameters. The retrieval results by different inversion methods may be improved with the suitable spectral subdomains and range of model parameters. Importantly, the proposed PROSDM showed the great potential to alleviate these negative effects on the retrievals of Cab, Cxc, Cw, and Cm. The performance assessment of PROSDM demonstrates a promising potential for its applications in remote sensing aiming at retrievals of leaf biochemical traits from leaf BRF spectra.

Validating the n-alkane technique for determining herbage dry matter intake in sheep offered perennial ryegrass harvested at varying growth stages and seasons

Optimising nutrition for animals requires accurate estimates of feed intake. In research studies determining feed intake in grazing animals can be problematic with many methods requiring individual housing. The n-alkane technique whereby long chain alkanes are used as internal markers for predicting dry matter intake (DMI), facilitates estimation of feed intake from animals while grazing. The aim of this study was to determine the suitability of the n-alkane technique for estimating grass DMI from sheep grazing perennial ryegrass at three different herbage masses and across three seasons. A total of 242 in vivo DMI measurements were obtained from 50 Texel wethers across spring, summer and autumn from 2012 to 2014 using the total collection method; whereby herbage offered and refused plus total faeces produced was measured for each animal daily. Animals were offered perennial ryegrass ad libitium which was harvested at three different pre-grazing herbage masses (HM; low (<1600 kg DM/ha), medium (1601−2999 kg DM/ha), and high (>3000 kg DM/ha), across three separate seasons (spring, summer and autumn). All animals were dosed once daily with a C32 n-alkane bolus for 12 days with a subsample of faeces collected for the final six days of each measurement phase. Lin’s concordance correlations were used to examine the relationship between each n-alkane pair when predicting DMI. The Lin’s concordance correlation coefficients between n-alkane estimated DMI using the ratio of C31 / C32 and C33 / C32 and measured DMI were 0.46 and 0.59, respectively. The r-squared value between measured DMI and n-alkane estimated DMI was 0.76 using the n-alkane pair C31 / C32 and 0.79 for the n-alkane pair C33 / C32. Herbage mass affected DMI with animals offered the low HM treatment having higher intakes (P < 0.05). Offering animals low and medium HM provided a more accurate estimate of DMI using the n-alkane technique. Thus, this research provides evidence that the n-alkane technique can be used as a reliable predictor of perennial ryegrass intake in sheep, when offered HM less than 2999 kg DM/ha.

Profiling of Brevetoxin Metabolites Produced by Karenia brevis 165 Based on Liquid Chromatography-Mass Spectrometry.

In this study, Karenia brevis 165 (K. brevis 165), a Chinese strain, was used to research brevetoxin (BTX) metabolites. The sample pretreatment method for the enrichment of BTX metabolites in an algal culture medium was improved here. The method for screening and identifying intracellular and extracellular BTX metabolites was established based on liquid chromatography-time-of-flight mass spectrometry (LC-ToF-MS) and liquid chromatography triple quadrupole tandem mass spectrometry (LC-QqQ-MS/MS). The results show that the recovery rates for BTX toxins enriched by a hydrophilic–lipophilic balance (HLB) extraction column were higher than those with a C18 extraction column. This method was used to analyze the profiles of extracellular and intracellular BTX metabolites at different growth stages of K. brevis 165. This is the first time a Chinese strain of K. brevis has been reported that can produce toxic BTX metabolites. Five and eight kinds of BTX toxin metabolites were detected in the cell and culture media of K. brevis 165, respectively. Brevenal, a toxic BTX metabolite antagonist, was found for the first time in the culture media. The toxic BTX metabolites and brevenal in the K. brevis 165 cell and culture media were found to be fully proven in terms of the necessity of establishing a method for screening and identifying toxic BTX metabolites. The results found by qualitatively and quantitatively analyzing BTX metabolites produced by K. brevis 165 at different growth stages show that the total toxic BTX metabolite contents in single cells ranged between 6.78 and 21.53 pg/cell, and the total toxin concentration in culture media ranged between 10.27 and 449.11 μg/L. There were significant differences in the types and contents of toxic BTX metabolites with varying growth stages. Therefore, when harmful algal blooms occur, the accurate determination of BTX metabolite types and concentrations will be helpful to assess the ecological disaster risk in order to avoid hazards and provide appropriate disaster warnings.

Application of deep learning to detect Lamb’s quarters (Chenopodium album L.) in potato fields of Atlantic Canada

Excessive use of herbicides for weed control increases the cost of crop production and can lead to environmental degradation. An intelligent spraying system can apply agrochemicals on an as-needed basis by detecting and selectively targeting the weeds. The objective of this research was to investigate the feasibility of using deep convolutional neural networks (DCNNs) for detecting lamb’s quarters (Chenopodium album) in potato fields. Five potato fields were selected in Prince Edward Island (PEI) and New Brunswick (NB), Canada to collect images of spatially and temporally varied potato plants and lamb’s quarters. The image database included pictures, taken under varying growth stages of potato, outdoor light (clear, cloudy, and partly cloudy), and shadowy conditions. The images were trained for DCNN models, namely GoogLeNet, VGG-16, and EfficientNet to classify lamb’s quarters and potato plants. Performance of two frameworks, namely TensorFlow and PyTorch, were compared in training, testing, and during inferring the DCNNs. Results showed excellent performance of DCNNs in lamb’s quarters and potato plant classification (accuracy > 90%). However, the EfficientNet with PyTorch framework showed a maximum accuracy of (0.92–0.97) for every growth stage of the plants. Inference times of DCNNs were recorded using three graphics processing units (GPUs), namely Nvidia GeForce 930MX, Nvidia GeForce GTX1080 Ti, and Nvidia GeForce GTX1050. All the DCNNs performed better with PyTorch than TensorFlow frameworks. It was concluded that the trained models can be used in automation of the spraying systems for the site-specific application of agrochemicals for weed control in potato fields. Such precision agriculture technologies will ensure economically viable and environmentally safe potato cultivation.

Relative abundance of ureides differs among plant fractions in soybean

The present study aims to compare the relative abundance of ureides (RAU) among soybean [Glycine max (L.) Merr.] plant fractions in contrasting trial settings and under three nitrogen (N) rates, designed to create variability on symbiotic N fixation. A greenhouse and a field experiment evaluated the RAU in plant fractions (base of the main stem, petiole and leaflets of the uppermost fully developed leaf) at varying growth stages and under absent, intermediate, or high N fertilization rates. The RAU was determined by measuring dry tissue nitrate and ureides and considered the entire main stem as benchmarking plant fraction, previously calibrated based on 15N isotope methods. The main outcomes of this study were: 1) RAU in the base of the stem accurately reflected the one obtained in the main stem; 2) upper leaflets RAU poorly correlate with the main stem values; and 3) the RAU from the upper petiole presented similar trends as the base of the stem but did not provide as reliable RAU estimation. The base of the stem is a reliable fraction to quantify soybean RAU while simplifying dry mass collection. The upper petiole fraction did not estimate RAU accurately, but it could differentiate contrasting N fixation levels.

High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding.

Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.

Effect of Growth Stage on Cotton Response to a Sublethal Concentration of Dicamba

AbstractThe introduction of auxin herbicide weed control systems has led to increased occurrence of crop injury in susceptible soybeans and cotton. Off-target exposure to sublethal concentrations of dicamba can occur at varying growth stages, which may affect crop response. Field experiments were conducted in Mississippi in 2014, 2015, and 2016 to characterize cotton response to a sublethal concentration of dicamba equivalent to 1/16X the labeled rate. Weekly applications of dicamba at 35 g ae ha−1were made to separate sets of replicated plots immediately following planting until 14 wk after emergence (WAE). Exposure to dicamba from 1 to 9 WAE resulted in up to 32% visible injury, and exposure from 7 to 10 WAE delayed crop maturity. Exposure from 8 to 10 and 13 WAE led to increased cotton height, while an 18% reduction in machine-harvested yield resulted from exposure at 6 WAE. Cotton exposure at 3 to 9 WAE reduced the seed cotton weight partitioned to position 1 fruiting sites, while exposure at 3 to 6 WAE also reduced yield in position 2 fruiting sites. Exposure at 2, 3, and 5 to 7 WAE increased the percent of yield partitioned to vegetative branches. An increase in percent of yield partitioned to plants with aborted terminals occurred following exposure from 3 to 7 WAE and corresponded with reciprocal decreases in yield partitioned to positional fruiting sites. Minimal effects were observed on fiber quality, except for decreases in fiber length uniformity resulting from exposure at 9 and 10 WAE.

Blood profile of broiler chickens fed diets supplemented with aged garlic extract or humates with probiotics at different growth stages

The study was conducted to determine the effects of supplementing aged garlic extract (AGE) and humates with probiotics (HWP), at varying growth stages, on the blood profile of Cobb 500 broilers. The experiment followed the 4x4 factorial in Completely Randomized Design (CRD). The feed supplement was used as factor A (negative control, with AGE, with HWP, and positive control or antibacterial), and the different growth stage as the factor B (brooder stage/0-12 d, starter stage/13-21 d, grower stage/22-28 d, and brooder to grower stages/0-28 d). A total of 320 male day-old Cobb broiler chicks (DOC) were randomly distributed to 16 groups with four replicates each. The supplements were given at 1g/kg inclusion rate. Supplementation has no effect on the erythrocytic indices, specifically the RBC count, hemoglobin, packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). The results showed that with the supplementation of AGE and HWP, the erythrocytic indices were maintained within the physiological limits. Thus, AGE and HWP supplementation has no adverse effect on the blood profile of Cobb 500 broiler.

Effect of Drought on Agronomic Traits of Rice and Wheat: A Meta-Analysis.

Drought has been one of the most important limiting factors for crop production, which deleteriously affects food security worldwide. The main objective of the present study was to quantitatively assess the effect of drought on the agronomic traits (e.g., plant height, biomass, yield, and yield components) of rice and wheat in combination with several moderators (e.g., drought stress intensity, rooting environment, and growth stage) using a meta-analysis study. The database was created from 55 published studies on rice and 60 published studies on wheat. The results demonstrated that drought decreased the agronomic traits differently between rice and wheat among varying growth stages. Wheat and rice yields decreased by 27.5% and 25.4%, respectively. Wheat grown in pots showed greater decreases in agronomic traits than those grown in the field. Rice showed opposite growing patterns when compared to wheat in rooting environments. The effect of drought on rice increased with plant growth and drought had larger detrimental influences during the reproductive phase (e.g., blooming stage, filling stage, and maturity). However, an exception was found in wheat, which had similar decreased performance during the complete growth cycle. Based on these results, future droughts could produce lower yields of rice and wheat when compared to the current drought.

Intoxicação por Senecio brasiliensis em bezerros: padrão e evolução de lesões hepáticas

RESUMO: Intoxicações por plantas do gênero Senecio representam uma importante causa de morte em bovinos no Rio Grande do Sul e estima-se que mais de 50% das mortes causadas por plantas tóxicas devem-se à intoxicação por Senecio spp. Nesse trabalho, são descritos os aspectos epidemiológicos, clínicos e anatomopatológicos de um surto de intoxicação natural por Senecio brasiliensis em bezerros e a caracterização e evolução das lesões hepáticas durante o surto. Um lote com 206 bovinos, fêmeas, sem raça definida, foi desmamado com aproximadamente quatro meses de idade e soltos em campo nativo com 25 hectares, contendo grande quantidade de Senecio brasiliensis em diferentes estágios. Os bovinos permaneceram nesse campo durante três meses e posteriormente foram transferidos para outra área. Na nova área, seis bezerras, com idades entre sete a oito meses, começaram a apresentar sinais clínicos de emagrecimento, fraqueza, diarreia e edema subcutâneo de declive na região submandibular. Os seis bovinos morreram em um período de 15 dias e um deles foi necropsiado. Macroscopicamente, foi visualizado o primeiro dos três padrões distintos de lesão hepática dos bovinos desse surto, que se caracterizava por um fígado aumentado de tamanho, com os bordos arredondados e com superfície capsular e de corte alaranjada. Histologicamente havia acentuada degeneração vacuolar hepatocelular difusa, bilestase, megalocitose e leve proliferação de ductos biliares e fibrose periportal. Dois meses após o início do surto, constatou-se que em um período de 20 dias adoeceram mais 28 bezerras e, dessas, 14 morreram. Os bovinos acometidos nessa segunda visita tinham idades entre nove a 10 meses. O principal sinal clínico observado foi fotossensibilização, e incluía graus variados de fotofobia, lacrimejamento, salivação excessiva, descarga ocular mucopurulenta, secreção nasal e glossite diftérica ventral. Das 14 bezerras mortas, realizou-se a necropsia de duas delas, sendo visualizado o segundo padrão de lesão hepática nesse surto (Padrão 2). Os fígados estavam com a superfície capsular lisa e esbranquiçada e moderadamente diminuídos de tamanho. Ao corte observou-se leve irregularidade do parênquima e áreas mais claras intercaladas com áreas avermelhadas. O aspecto histológico incluía acentuada fibrose periportal, marcada proliferação de ductos biliares, megalocitose, áreas multifocais de necrose de hepatócitos, hemorragia e lesões veno-oclusivas. A atividade sérica da GGT foi avaliada em 15 bovinos afetados nessa segunda visita à propriedade. Desses, 13 bezerras testadas apresentaram alterações nos valores, que variaram de 26-175 U/L. Em uma terceira visita à propriedade, sete meses após o início do surto, mais 27 bezerras adoeceram em um período de 40 dias e, dessas, 23 morreram, sendo três submetidas à necropsia. Evidenciou-se o terceiro padrão de lesão hepática. O fígado dos três bovinos estava diminuído de tamanho e acentuadamente firme. A superfície capsular era lisa e esbranquiçada, e a superfície de corte apresentava-se difusamente acastanhada. Lesões histológicas semelhantes ao segundo padrão foram também constatadas no fígado dessas três bezerras. Na quarta visita à propriedade, realizaram-se biópsias hepáticas nos 163 bovinos restantes do lote. Lesões hepáticas características da intoxicação estavam presentes em 103 dos 163 bovinos submetidos à biopsia hepática. Os bovinos afetados foram classificados em grupos de acordo com a severidade e os aspectos morfológicos observados. O diagnóstico de intoxicação nos bovinos desse estudo baseou-se nos achados epidemiológicos, clínicos e anatomopatológicos, associados ao histórico do consumo da planta e evolução das lesões.

Genetic improvement of rice crop under high temperature stress: bridging plant physiology with molecular biology

Rice crop meets the calorific needs of the masses. High temperature stress has negative effects on the physiology and biochemistry of this crop. This is amply reflected from studies showing that heat stress has strong detrimental effects on photosynthetic efficiency, electrolyte leakage, lipid peroxidation, enzyme (superoxide dismutase, peroxidase, catalase, etc.) activities and transpiration rate at varied growth stages, ultimately affecting the total harvest of this crop. The macro cellular- and plant-level changes as of aboveare rooted inalterations at the molecular level, affecting the gene expression profiles, protein abundance and their interaction at the genomic level. With global warming at its pace, the cultivation of rice under field conditions is greatly threatened. Molecular analyses show that the response of rice to heat stress involves a host of genes including transcription factors, genes involved in calcium and hormonal signalling, reactive oxygen species metabolism genes and chaperones. Heat shock proteins (Hsps) are considered the key players with role in maintaining proteostasis under heat and related stresses. The expression of Hsps is centrally governed by heat shock factors (Hsfs). Hsps and Hsfs have emerged as the potential candidates for engineering stress tolerance in crop plants. Several attempts have been made in which rice genes have been incorporated in varied hosts and concurrently rice has been used as trans-host with various genes cloned from rice and other plant species. In this review, we provide an account of physiological, biochemical, transcriptomic and proteomic responses of rice crop to heat stress. The importance of ClpB-C/Hsp100 gene in heat stress tolerance and the potential heat shock factor(s) governing regulation of ClpB-C/Hsp100 for engineering heat stress tolerance in rice has been discussed.