Effects of 1-methylcyclopropene (1-MCP) on Growth, Yield, and Physiological Parameters of Field Grown Cotton (Gossypium hirsutum L.)

Cotton (Gossypium hirsutum L.) crops usually experience some type of environmental stress during the season. Soil moisture deficits along with high temperatures pose the biggest constraints for crop productivity. Although usually hard to distinguish between drought and high temperature stress effects, it is important to develop means to help mitigate the negative impacts of such stresses on crop productivity. The 1-methylcyclopropene (1-MCP) is an ethylene antagonist that acts by binding to ethylene receptors, thus delaying and/or diminishing its effects on plants. Recently 1-MCP became the focus of several studies due to its potential to mitigate negative impacts of abiotic stresses. The main objective of this research was to assess the impact of 1-MCP on field grown cotton. The secondary objective was to investigate the association of canopy temperature (CT), canopy temperature depression (CTD), stress degree day (SDD), thermal stress index (TSI), and crop water stress index (CWSI) with crop yield. Field studies were conducted at the Texas A&M University Field Laboratory in Burleson County, TX from 2012 to 2014. Plots were arranged in a randomized complete block design and replicated four times. Treatments consisted of 1-MCP application (25 g a.i. ha^-1) triggered by canopy temperature (28 °C) and forecasted ambient temperatures (35 and 27.8 °C). For the secondary objective treatments were two irrigation levels, namely, dryland and irrigated. Results indicated that 1-MCP had little to no effect on the physiology and morphology of cotton at different stages of crop development. Daily plant canopy temperature, net photosynthesis, transpiration, and photosystem II quantum yield were affected by 1-MCP treatment when plants were irrigated, but not under dryland conditions. Effects of 1-MCP applications during different seasons were inconsistent. Ultimately, 1-MCP treatment effects were not enough to increase final seedcotton yield under the conditions tested. Negative relationships between yield and CT (r^2 = 0.66), yield and TSI (r^2 = 0.70), and yield and CWSI (r^2 = 0.58) were found. CTD and SDD showed great distinction between the humid (2012 and 2014) and dry (2013) years, and to a lesser extent, this was also apparent for CWSI. Evidence suggests that CTD, SDD, and CWSI models should be interpreted with caution, particularly in locations where great inter-annual weather variability occurs

Advanced Analysis of the Responses of Cotton Genotypes Growing Under Water Stress

The ever-growing world population raises the concern and necessity of rational use and distribution of limited water resources. Water deficit is the single most dominant abiotic factor limiting cotton (Gossypium hirsutum L.) yield in drought-prone Texas croplands. Characterizing plant traits conferring drought tolerance to cotton genotypes and then transferring this information back to breeders and geneticists have the potential of significantly increasing and stabilizing production statewide. Although a plethora of physiological studies have been conducted and have demonstrated that drought tolerance in plants is likely to be conferred by a combination of plant traits rather than a single trait, this knowledge has not translated into improved breeding lines. Experiments were conducted in 2010 and 2011 in the Drought Tolerance Laboratory (Texas AgriLife Research and Extension Center in Corpus Christi, TX) to analyze the responses of cotton genotypes to different levels of water stress. This facility is equipped with computerized systems capable of continuously monitoring whole-plant water use as well as several environmental parameters. Sixteen cotton genotypes were provided by Monsanto Co. and the Texas AgriLife Cotton Improvement Programs at College Station and Lubbock. Seeds were pre-germinated in wet paper towels and then hand planted in large pots previously filled with fritted clay. A total of 3 and 8 (2010 and 2011, respectively) pots containing plants of each genotype were permanently placed on micro-lysimeters for continuous measurement of water use. Water regimes were imposed in 2010 (well-watered and water-stressed), and 2011 (water-stressed) when plants reached the early-flowering stage and were carried until plants reached maturity (100% open bolls). Data collected showed that genotypes have very distinct water use patterns. The water stress treatment imposed on the test plants negatively affected plant growth that was indicated by a lower plant height, total number of leaves, and main-stem nodes of stressed plants when contrasted to their well-watered counterparts. Stomatal density was remarkably different among genotypes and a higher density was found on the abaxial (lower) leaf surface for all genotypes studied. Root dry mass production had different responses depending upon the severity of the water stress. Highest root dry mass was observed when plants were exposed to a mild stress and lowest when a more severe water restriction was imposed

Trends in studies of Brazilian stream fish assemblages

Studies about fish assemblages in Brazilian streams have grown in recent years, however, it remains unclear whether this increase is followed by increments in the diversity of addressed topics and theoretical frameworks adopted by researchers. We performed a systematic search for Brazilian studies on stream fish assemblages recording study region, publication year, objectives, and spatial and temporal scales adopted. The number of studies is unevenly distributed among regions. Most papers describe the general structure of local fish assemblages and their scientific objectives have not varied through time. Studies have been conducted mainly at small temporal and spatial scales, though the latter is increasing over time. We argue for the need of focusing on recently developed ecological theories and frameworks, and expanding the temporal and spatial scales of studies. These changes will improve regional and local conservation policies, and the visibility of aquatic Brazilian research in the global scientific community. © 2016 Associação Brasileira de Ciência Ecológica e Conservaçã

Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension

OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo

Minimal Symptom Expression' in Patients With Acetylcholine Receptor Antibody-Positive Refractory Generalized Myasthenia Gravis Treated With Eculizumab

The efficacy and tolerability of eculizumab were assessed in REGAIN, a 26-week, phase 3, randomized, double-blind, placebo-controlled study in anti-acetylcholine receptor antibody-positive (AChR+) refractory generalized myasthenia gravis (gMG), and its open-label extension

Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture

Unmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In this study, we investigated the difference in tillage treatments for cotton and sorghum using various RGB and NIR VIs. Minimized tillage has been known to increase farm sustainability and potentially optimize productivity over time; however, repeated tillage is the most commonly-adopted management practice in agriculture. To this day, quantitative comparisons of plant growth patterns between conventional tillage (CT) and no tillage (NT) fields are often inconsistent. In this study, high-resolution and multi-temporal UAV data were used for the analysis of tillage effects on plant health and the performance of various vegetation indices investigated. Time series data over ten dates were acquired on a weekly basis by RGB and multispectral (MS) UAV platforms: a DJI Phantom 4 Pro and a DJI Matrice 100 with the SlantRange 3p sensor. Ground reflectance panels and an ambient illumination sensor were used for the radiometric calibration of RGB and MS orthomosaic images, respectively. Various RGB and NIR-based vegetation indices were then calculated for the comparison between CT and NT treatments. In addition, a one-tailed Z-test was conducted to check the significance of VIs’ difference between CT and NT treatments. The results showed distinct differences in VIs between tillage treatments during the whole growing season. NIR-based VIs showed better discrimination performance than RGB-based VIs. Out of 13 VIs, the modified soil adjusted vegetation index (MSAVI) and optimized soil adjusted vegetation index (OSAVI) showed better performance in terms of quantitative difference measurements and the Z-test between tillage treatments. The modified green red vegetation index (MGRVI) and excess green (ExG) showed reliable separability and can be an alternative for economic RGB UAV application.Unmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In this study, we investigated the difference in tillage treatments for cotton and sorghum using various RGB and NIR VIs. Minimized tillage has been known to increase farm sustainability and potentially optimize productivity over time; however, repeated tillage is the most commonly-adopted management practice in agriculture. To this day, quantitative comparisons of plant growth patterns between conventional tillage (CT) and no tillage (NT) fields are often inconsistent. In this study, high-resolution and multi-temporal UAV data were used for the analysis of tillage effects on plant health and the performance of various vegetation indices investigated. Time series data over ten dates were acquired on a weekly basis by RGB and multispectral (MS) UAV platforms: a DJI Phantom 4 Pro and a DJI Matrice 100 with the SlantRange 3p sensor. Ground reflectance panels and an ambient illumination sensor were used for the radiometric calibration of RGB and MS orthomosaic images, respectively. Various RGB and NIR-based vegetation indices were then calculated for the comparison between CT and NT treatments. In addition, a one-tailed Z-test was conducted to check the significance of VIs’ difference between CT and NT treatments. The results showed distinct differences in VIs between tillage treatments during the whole growing season. NIR-based VIs showed better discrimination performance than RGB-based VIs. Out of 13 VIs, the modified soil adjusted vegetation index (MSAVI) and optimized soil adjusted vegetation index (OSAVI) showed better performance in terms of quantitative difference measurements and the Z-test between tillage treatments. The modified green red vegetation index (MGRVI) and excess green (ExG) showed reliable separability and can be an alternative for economic RGB UAV application

Automated open cotton boll detection for yield estimation using unmanned aircraft vehicle (UAV) data

Unmanned aerial vehicle (UAV) images have great potential for various agricultural applications. In particular, UAV systems facilitate timely and precise data collection in agriculture fields at high spatial and temporal resolutions. In this study, we propose an automatic open cotton boll detection algorithm using ultra-fine spatial resolution UAV images. Seed points for a region growing algorithm were generated hierarchically with a random base for computation efficiency. Cotton boll candidates were determined based on the spatial features of each region growing segment. Spectral threshold values that automatically separate cotton bolls from other non-target objects were derived based on input images for adaptive application. Finally, a binary cotton boll classification was performed using the derived threshold values and other morphological filters to reduce noise from the results. The open cotton boll classification results were validated using reference data and the results showed an accuracy higher than 88% in various evaluation measures. Moreover, the UAV-extracted cotton boll area and actual crop yield had a strong positive correlation (0.8). The proposed method leverages UAV characteristics such as high spatial resolution and accessibility by applying automatic and unsupervised procedures using images from a single date. Additionally, this study verified the extraction of target regions of interest from UAV images for direct yield estimation. Cotton yield estimation models had R2 values between 0.63 and 0.65 and RMSE values between 0.47 kg and 0.66 kg per plot grid.Unmanned aerial vehicle (UAV) images have great potential for various agricultural applications. In particular, UAV systems facilitate timely and precise data collection in agriculture fields at high spatial and temporal resolutions. In this study, we propose an automatic open cotton boll detection algorithm using ultra-fine spatial resolution UAV images. Seed points for a region growing algorithm were generated hierarchically with a random base for computation efficiency. Cotton boll candidates were determined based on the spatial features of each region growing segment. Spectral threshold values that automatically separate cotton bolls from other non-target objects were derived based on input images for adaptive application. Finally, a binary cotton boll classification was performed using the derived threshold values and other morphological filters to reduce noise from the results. The open cotton boll classification results were validated using reference data and the results showed an accuracy higher than 88% in various evaluation measures. Moreover, the UAV-extracted cotton boll area and actual crop yield had a strong positive correlation (0.8). The proposed method leverages UAV characteristics such as high spatial resolution and accessibility by applying automatic and unsupervised procedures using images from a single date. Additionally, this study verified the extraction of target regions of interest from UAV images for direct yield estimation. Cotton yield estimation models had R2 values between 0.63 and 0.65 and RMSE values between 0.47 kg and 0.66 kg per plot grid

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive