Triploid watermelon response to flumioxazin

2021 ◽  
pp. 1-18
Author(s):  
Stephen L. Meyers ◽  
Wenjing Guan ◽  
Dan Egel ◽  
Dennis Nowaskie
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Cover ◽  
Field Trials ◽  
Marketable Yield ◽  
Broadcast Application ◽  
Herbicide Treatments ◽  
Triploid Watermelon

Abstract Field trials were conducted in 2016 and 2017 at the Southwest Purdue Agricultural Center in Vincennes, IN to determine the tolerance of plasticulture-grown ‘Fascination’ triploid watermelon to flumioxazin. Treatments were applied after plastic was laid, but one day prior to transplanting and consisted of row middle applications of clomazone (210 g ai ha−1) plus ethafluralin (672 g ai ha−1), flumioxazin (107 g ai ha−1), and flumioxazin (88 g ha−1) plus pyroxasulfone (112 g ai ha−1), a broadcast application of flumioxazin (107 g ha−1), and a non-treated check. Compared to the non-treated check, the broadcast application of flumioxazin reduced watermelon vine length and normalized difference vegetation index (NDVI) values. All other herbicide treatments had vine length and NDVI values similar to the non-treated check. At 25/26 d after transplanting (DAP), weedy ground cover in row middles of the non-treated check was 39% and 14% in 2016 and 2017, respectively. Weedy ground cover in herbicide-containing treatments was significantly less, ≤7% and ≤5% in 2016 and 2017, respectively. Marketable watermelon yield of the non-treated check was 77,931 kg and 11,115 fruits ha−1. The broadcast application of flumioxazin resulted in reduced marketable yield (64,894 kg ha−1) and fewer fruit (9,550 ha−1).

scholarly journals Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

2021 ◽  
Vol 13 (21) ◽  
pp. 4466
Author(s):  
Isabell Eischeid ◽  
Eeva M. Soininen ◽  
Jakob J. Assmann ◽  
Rolf A. Ims ◽  
Jesper Madsen ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Predictive Ability ◽  
Ground Cover ◽  
Arctic Tundra ◽  
The Arctic ◽  
Ecosystem Monitoring ◽  
Selection Of Variables ◽  
State Changes ◽  
Study Sites

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs.

scholarly journals Vegetation dynamics in Alpine glacier forelands tackled from space

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea Fischer ◽  
Thomas Fickert ◽  
Gabriele Schwaizer ◽  
Gernot Patzelt ◽  
Günther Groß
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Cover ◽  
Plant Succession ◽  
Ice Age ◽  
Hazard Management ◽  
Alpine Glacier ◽  
Glacier Terminus ◽  
Vegetation Sampling

Abstract Monitoring of plant succession in glacier forelands has so far been restricted to field sampling. In this study, in situ vegetation sampling along a chronosequence between Little Ice Age (LIA) maximum extent and the recent glacier terminus at Jamtalferner in the Austrian Alps is compared to time series of the Normalized Difference Vegetation Index (NDVI) calculated from 13 Landsat scenes (1985–2016). The glacier terminus positions at 16 dates between the LIA maximum and 2015 were analysed from historical maps, orthophotos and LiDAR images. We sampled plots of different ages since deglaciation, from very recent to approx. 150 years: after 100 years, roughly 80% of the ground is covered by plants and ground cover does not increase significantly thereafter. The number of species increases from 10–20 species on young sites to 40–50 species after 100 years. The NDVI increases with the time of exposure from a mean of 0.11 for 1985–1991 to 0.20 in 2009 and 0.27 in 2016. As the increase in ground cover is clearly reproduced by the NDVI (R² ground cover/NDVI 0.84) – even for sparsely vegetated areas –, we see a great potential of satellite-borne NDVI to perform regional characterizations of glacier forelands for hydrological, ecological and hazard management-related applications.

Reduced herbicide rates for control of living mulch and weeds in fresh market tomato

2019 ◽  
Vol 34 (1) ◽  
pp. 55-63
Author(s):  
Vinay Bhaskar ◽  
Robin R. Bellinder ◽  
Stephen Reiners ◽  
Antonio DiTommaso
Keyword(s):  
Ground Cover ◽  
Field Trials ◽  
Residual Soil ◽  
Fresh Market ◽  
Living Mulch ◽  
Cash Crop ◽  
Tomato Yield ◽  
Herbicide Treatments ◽  
Sunn Hemp ◽  
Soil Activity

AbstractLiving mulches can provide many sustainability benefits. However, living mulch–cash crop competition and unreliable weed control are major challenges in living mulch systems. In this study, we evaluated the potential of herbicides used at reduced rates in combination with living mulch to suppress weeds, while simultaneously reducing living mulch vigor. Herbicide treatments were a combination of two POST applications, each consisting of a single, different herbicide. Field trials were conducted in Freeville, NY, USA, using: fresh market field tomato as cash crop; sesbania and sunn hemp as living mulch; and the herbicides fomesafen, halosulfuron, metribuzin, and rimsulfuron. In 2015, when water was not limiting, tomato yield and living mulch biomass were positively correlated. This relationship was negative in 2016, likely because of drought during the growing season. Compared with the untreated living mulch check, using the herbicide treatments in combination with living mulch reduced tomato yield losses by up to 71% in 2015 and 51% in 2016. In these herbicide plus living mulch plots, weed biomass was reduced by up to 97%, compared with the weedy check. Living mulch in herbicide treatments generated up to 2500 kg ha−1 of dry matter during both 2015 and 2016, with an average ground cover of 63% in 2015 and 85% in 2016. A predominantly PRE herbicide with residual soil activity (metribuzin), followed by a herbicide with greater POST activity (halosulfuron/rimsulfuron) was the most effective herbicide application sequence. Results from our study indicate that well-designed herbicide applications may enhance the practicability of living mulch systems.

scholarly journals Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
David M. Deery ◽  
David J. Smith ◽  
Robert Davy ◽  
Jose A. Jimenez-Berni ◽  
Greg J. Rebetzke ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Reliability ◽  
Stem Elongation ◽  
Digital Camera ◽  
Ground Cover ◽  
Growth Stages ◽  
Rgb Images ◽  
High Degree ◽  
Pearson Correlations

Canopy ground cover (GC) is an important agronomic measure for evaluating crop establishment and early growth. This study evaluates the reliability of GC estimates, in the presence of varying light and dew on leaves, from three different ground-based sensors: (1) normalized difference vegetation index (NDVI) from the commercially available GreenSeeker®; (2) RGB images from a digital camera, where GC was determined as the portion of pixels from each image meeting a greenness criterion (i.e., Green−Red/Green+Red>0); and (3) LiDAR using two separate approaches: (a) GC from LiDAR red reflectance (whereby red reflectance less than five was classified as vegetation) and (b) GC from LiDAR height (whereby height greater than 10 cm was classified as vegetation). Hourly measurements were made early in the season at two different growth stages (tillering and stem elongation), among wheat genotypes highly diverse for canopy characteristics. The active NDVI showed the least variation through time and was particularly stable, regardless of the available light or the presence of dew. In addition, between-sample-time Pearson correlations for NDVI were consistently high and significant (P<0.0001), ranging from 0.89 to 0.98. In comparison, GC from LiDAR and RGB showed greater variation across sampling times, and LiDAR red reflectance was strongly influenced by the presence of dew. Excluding times when the light was exceedingly low, correlations between GC from RGB and NDVI were consistently high (ranging from 0.79 to 0.92). The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.

Sensitivities of Normalized Difference Vegetation Index and a Green/Red Ratio Index to Cotton Ground Cover Fraction

Crop Science ◽  
2010 ◽  
Vol 50 (3) ◽  
pp. 1000-1010 ◽  
Author(s):  
G. L. Ritchie ◽  
D. G. Sullivan ◽  
W. K. Vencill ◽  
C. W. Bednarz ◽  
J. E. Hook
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Cover

scholarly journals CHANGE DETECTION IN BRAZILIAN SAVANNAS USING SEMIVARIOGRAMS DERIVED FROM NDVI IMAGES

2015 ◽  
Vol 39 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Fausto Weimar Acerbi Júnior ◽  
Eduarda Martiniano de Oliveira Silveira ◽  
José Márcio de Mello ◽  
Carlos Rogério de Mello ◽  
José Roberto Soares Scolforo
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Photosynthetic Activity ◽  
Ground Cover ◽  
Clear Trend ◽  
Area Index ◽  
Exploratory Data ◽  
Vegetation Parameters ◽  
Analysis Models ◽  
Best Fit

The Normalized Difference Vegetation Index (NDVI) is often used to extract information from vegetated areas since it is directly related to vegetation parameters such as percent of ground cover, photosynthetic activity of the plant and leaf area index. The aim of this paper was to analyze the potencial of semivariograms generated from NDVI values to detect changes in vegetated areas, analyzing their behavior (shape) and derived metrics (range, sill and nugget). Semivariograms were generated from NDVI values derived from Landsat TM images of May 2010, June 2010 and July 2011. The study area is located in the northern state of Minas Gerais, Brazil, and is covered by Brazilian savannas vegetation, totalizing 1,596 ha. Semivariograms were generated after the exploratory data analysis. Models were fitted, validated and their metrics analyzed. The results showed a very clear trend where the shape of semivariograms, sill and range were different when deforestation occurred and were similar when the area had not been changed. The model that generated best fit was the Gaussian, however, the three models tested showed behavior that makes it possible to detect changes in vegetation. It suggests that further researches should explore the degree to which the semivariogram can be used to quantify this spatial variability as well as to analyze the influence of sazonality for changing detection in vegetated areas.

scholarly journals Effects of Morning and Afternoon Shade in Combination with Traffic Stress on Seashore Paspalum

HortScience ◽  
2003 ◽  
Vol 38 (6) ◽  
pp. 1218-1222 ◽  
Author(s):  
Yiwei Jiang ◽  
Robert N. Carrow ◽  
Ronny R. Duncan
Keyword(s):  
Soil Compaction ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tissue Injury ◽  
Field Trials ◽  
Stress Index ◽  
Lower Stress ◽  
Seashore Paspalum ◽  
Athletic Fields ◽  
Band Index

Turfgrasses are often exposed to different shade environments in conjunction with traffic stresses (wear and/or compaction) in athletic fields within stadiums. The objective of this study was to assess the effects of morning shade (AMS) and afternoon shade (PMS) alone and in combination with wear and wear plus soil compaction on `Sea Isle 1 seashore paspalum (Paspalum vaginatum Swartz). The study was conducted using two consecutive field trials under sports field conditions from 9 July to 10 Sept. 2001 at the Univ. of Georgia Experiment Station at Griffin. “T” shaped structures constructed of plywood on the sports field were used to provide §90% morning and afternoon shade, respectively, and were in place for 1 year prior to data accumulation. A wear device and a studded roller device simulated turfgrass wear (WD) and wear plus soil compaction (WSC), respectively, to the shaded plots. Only minor differences in turf color, density, or canopy spectral reflectance were found between AMS and PMS under no-traffic treatments in both trials. Grasses under WD generally recovered faster than those exposed to WSC across all light levels, including full sunlight (FL), AMS, and PMS. AMS combined with WD treatment had an average 9% higher rating of color, 11% higher density, and 28% less tissue injury than that of PMS with WD at 7 days after traffic treatment (DAT). Compared to PMS with WSC treatment at 7 DAT, AMS with WSC had 12% higher rating of color, 9% higher density, and 4% less tissue injury. AMS with WD treatment exhibited 11% higher normalized difference vegetation index (NDVI), 4% higher canopy water band index (CWBI), and 13% lower stress index than that of PMS with WD at 7 DAT. AMS with WSC, relative to PMS with WSC, demonstrated 8% higher NDVI, 3% higher CWBI, and 8% lower stress index at 7 DAT. Re sults indicated that AMS (i.e., afternoon sunlight) had less detrimental influences than PMS (i.e., morning sunlight) on turfgrass performance after it was subjected to wear stress or wear plus soil compaction.

scholarly journals Crop Sensor Based Non-destructive Estimation of Nitrogen Nutritional Status, Yield, and Grain Protein Content in Wheat

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 148 ◽  
Author(s):  
Marta Aranguren ◽  
Ander Castellón ◽  
Ana Aizpurua
Keyword(s):  
Protein Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Biomass ◽  
Nitrogen Nutrition ◽  
Stem Elongation ◽  
Field Trials ◽  
Grain Protein Content ◽  
Grain Protein ◽  
Single Model

Minimum NNI (Nitrogen Nutrition Index) values have been developed for each key growing stage of wheat (Triticum aestivum) to achieve high grain yields and grain protein content (GPC). However, the determination of NNI is time-consuming. This study aimed to (i) determine if the NNI can be predicted using the proximal sensing tools RapidScan CS-45 (NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge)) and Yara N-TesterTM and if a single model for several growing stages could be used to predict the NNI (or if growing stage-specific models would be necessary); (ii) to determine if yield and GPC can be predicted using both tools; and (iii) to determine if the predictions are improved using normalized values rather than absolute values. Field trials were established for three consecutive growing seasons where different N fertilization doses were applied. The tools were applied during stem elongation, leaf-flag emergence, and mid-flowering. In the same stages, the plant biomass was sampled, N was analyzed, and the NNI was calculated. The NDVI was able to estimate the NNI with a single model for all growing stages (R2 = 0.70). RapidScan indexes were able to predict the yield at leaf-flag emergence with normalized values (R2 = 0.70–0.76). The sensors were not able to predict GPC. Data normalization improved the model for yield but not for NNI prediction.

Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

2019 ◽  
Vol 2 (1) ◽  
pp. 11-14
Author(s):  
Wahyu Adi
Keyword(s):  
Coral Reefs ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Interpretation ◽  
Vegetation Coverage ◽  
Small Island ◽  
Shallow Waters ◽  
Small Islands ◽  
Seagrass Beds ◽  
Cover Density

Pulau Kecil Gelasa merupakan daerah yang belum banyak diteliti. Pemetaan ekosistem di pulau kecil dilakukan dengan bantuan citra Advanced Land Observing Satellite (ALOS). Penelitian terdahulu diketahui bahwa ALOS memiliki kemampuan memetakan terumbu karang dan padang lamun di perairan dangkal serta mampu memetakan kerapatan penutupan vegetasi. Metode interpretasi citra menggunakan alogaritma indeks vegetasi pada citra ALOS yaitu NDVI (Normalized Difference Vegetation Index), serta pendekatan Lyzengga untuk mengkoreksi kolom perairan. Hasil penelitian didapatkan luasan Padang Lamun di perairan dangkal 41,99 Ha, luasan Terumbu Karang 125,57 Ha. Hasil NDVI di daratan/ pulau kecil Gelasa untuk Vegetasi Rapat seluas 47,62 Ha; luasan penutupan Vegetasi Sedang 105,86 Ha; dan penutupan Vegetasi Jarang adalah 34,24 Ha.   Small Island Gelasa rarely studied. Mapping ecosystems on small islands with the image of Advanced Land Observing Satellite (ALOS). Previous research has found that ALOS has the ability to map coral reefs and seagrass beds in shallow water, and is able to map vegetation cover density. The method of image interpretation uses the vegetation index algorithm in the ALOS image, NDVI (Normalized Difference Vegetation Index), and the Lyzengga approach to correct the water column. The results of the study were obtained in the area of Seagrass Padang in the shallow waters of 41.99 ha, the area of coral reefs was 125.57 ha. NDVI results on land / small islands Gelasa for dense vegetation of 47.62 ha; area of Medium Vegetation coverage 105.86 Ha; and the coverage of Rare Vegetation is 34.24 Ha.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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