scholarly journals QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

2020 ◽  
Author(s):  
Rasyidah Mohamad Razar ◽  
Ali Missaoui
Keyword(s):  
Qtl Mapping ◽  
Panicum Virgatum ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Biomass Accumulation ◽  
Winter Dormancy ◽  
High Heritability ◽  
Pleiotropic Gene ◽  
Growth Changes

Abstract Background Switchgrass (Panicum virgatum) undergoes seasonal growth changes based on the perception of photoperiod and temperature. It transitions to winter dormancy in early fall, breaks dormancy in the spring, and resumes the cycle starting from flowering which is the cue for senescence. The length of the growing season can impact biomass accumulation and yield. In this study, we conducted QTL mapping of winter dormancy measured by fall regrowth height (FRH) and normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two bi-parental pseudo-F1 populations derived from crosses between the lowland AP13 with the lowland B6 (AB) with 285 progenies, and the lowland B6 with the upland VS16 (BV) with 227 progenies. Results We identified 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD. The percent variance explained by these QTLs ranged between 4.21 – 23.27% for FRH, 4.47 – 24.06% for NDVI, 4.35 – 32.77% for SE, and 4.61 – 29.74% for FD. A higher number of QTL was discovered in the BV population, suggesting more variants in the lowland x upland population contributing to the expression of seasonal dormancy underlying traits. We identified 9 regions of colocalized QTL with possible pleiotropic gene action. The positive correlation between FRH or NDVI with dry biomass weight suggests that winter dormancy duration could affect switchgrass biomass yield. The medium to high heritability levels of FRH (0.55 – 0.64 H2) and NDVI (0.30 – 0.61 H2) implies the possibility of using the traits for indirect selection for biomass yield. Conclusion Markers found within the significant QTL interval can serve as genomic resources for breeding non-dormant and semi-dormant switchgrass cultivars for the southern regions, where growers can benefit from the longer production season.

scholarly journals QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

2020 ◽  
Author(s):  
Rasyidah Mohamad Razar ◽  
Ali Missaoui
Keyword(s):  
Qtl Mapping ◽  
Panicum Virgatum ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Winter Dormancy ◽  
Temperature Changes ◽  
High Heritability ◽  
Pleiotropic Gene ◽  
Early Fall

Abstract Background Switchgrass (Panicum virgatum) undergoes winter dormancy by sensing photoperiod and temperature changes. It transitions to winter dormancy in early fall following at the end of reproduction and exits dormancy in the spring. The duration of the growing season affects the accumulation of biomass and yield. In this study, we conducted QTL mapping of winter dormancy measured by fall regrowth height (FRH) and normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two bi-parental pseudo-F1 populations derived from crosses between the lowland AP13 with the lowland B6 (AB) with 285 progenies, and the lowland B6 with the upland VS16 (BV) with 227 progenies. Results We identified 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD. The percent variance explained by these QTLs ranged between 4.21 – 23.27% for FRH, 4.47 – 24.06% for NDVI, 4.35 – 32.77% for SE, and 4.61 – 29.74% for FD. A higher number of QTL was discovered in the BV population, suggesting more variants in the lowland x upland population contributing to the expression of seasonal dormancy underlying traits. We identified 9 regions of colocalized QTL with possible pleiotropic gene action. The positive correlation between FRH or NDVI with dry biomass weight suggests that winter dormancy duration could affect switchgrass biomass yield. The medium to high heritability levels of FRH (0.55 – 0.64 H2) and NDVI (0.30 – 0.61 H2) implies the possibility of using the traits for indirect selection for biomass yield. Conclusion Markers found within the significant QTL interval can serve as genomic resources for breeding non-dormant and semi-dormant switchgrass cultivars for the southern regions, where growers can benefit from the longer production season.

scholarly journals QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rasyidah M. Razar ◽  
Ali Missaoui
Keyword(s):  
Qtl Mapping ◽  
Panicum Virgatum ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Winter Dormancy ◽  
Temperature Changes ◽  
High Heritability ◽  
Pleiotropic Gene ◽  
Early Fall

Abstract Background Switchgrass (Panicum virgatum) undergoes winter dormancy by sensing photoperiod and temperature changes. It transitions to winter dormancy in early fall following at the end of reproduction and exits dormancy in the spring. The duration of the growing season affects the accumulation of biomass and yield. In this study, we conducted QTL mapping of winter dormancy measured by fall regrowth height (FRH) and normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two bi-parental pseudo-F1 populations derived from crosses between the lowland AP13 with the lowland B6 (AB) with 285 progenies, and the lowland B6 with the upland VS16 (BV) with 227 progenies. Results We identified 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD. The percent variance explained by these QTLs ranged between 4.21–23.27% for FRH, 4.47–24.06% for NDVI, 4.35–32.77% for SE, and 4.61–29.74% for FD. A higher number of QTL was discovered in the BV population, suggesting more variants in the lowland x upland population contributing to the expression of seasonal dormancy underlying traits. We identified 9 regions of colocalized QTL with possible pleiotropic gene action. The positive correlation between FRH or NDVI with dry biomass weight suggests that winter dormancy duration could affect switchgrass biomass yield. The medium to high heritability levels of FRH (0.55–0.64 H2) and NDVI (0.30–0.61 H2) implies the possibility of using the traits for indirect selection for biomass yield. Conclusion Markers found within the significant QTL interval can serve as genomic resources for breeding non-dormant and semi-dormant switchgrass cultivars for the southern regions, where growers can benefit from the longer production season.

scholarly journals QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

2020 ◽  
Author(s):  
Rasyidah Mohamad Razar ◽  
Ali Missaoui
Keyword(s):  
Qtl Mapping ◽  
Panicum Virgatum ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Winter Dormancy ◽  
Temperature Changes ◽  
High Heritability ◽  
Pleiotropic Gene ◽  
Early Fall

Abstract BackgroundSwitchgrass (Panicum virgatum) undergoes winter dormancy by sensing photoperiod and temperature changes. It transitions to winter dormancy in early fall following at the end of reproduction and exits dormancy in the spring. The duration of the growing season affects the accumulation of biomass and yield. In this study, we conducted QTL mapping of winter dormancy measured by fall regrowth height (FRH) and normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two bi-parental pseudo-F1 populations derived from crosses between the lowland AP13 with the lowland B6 (AB) with 285 progenies, and the lowland B6 with the upland VS16 (BV) with 227 progenies. ResultsWe identified 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD. The percent variance explained by these QTLs ranged between 4.21 – 23.27% for FRH, 4.47 – 24.06% for NDVI, 4.35 – 32.77% for SE, and 4.61 – 29.74% for FD. A higher number of QTL was discovered in the BV population, suggesting more variants in the lowland x upland population contributing to the expression of seasonal dormancy underlying traits. We identified 9 regions of colocalized QTL with possible pleiotropic gene action. The positive correlation between FRH or NDVI with dry biomass weight suggests that winter dormancy duration could affect switchgrass biomass yield. The medium to high heritability levels of FRH (0.55 – 0.64 H2) and NDVI (0.30 – 0.61 H2) implies the possibility of using the traits for indirect selection for biomass yield. ConclusionMarkers found within the significant QTL interval can serve as genomic resources for breeding non-dormant and semi-dormant switchgrass cultivars for the southern regions, where growers can benefit from the longer production season.

scholarly journals QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

2020 ◽  
Author(s):  
Rasyidah Mohamad Razar ◽  
Ali Missaoui
Keyword(s):  
Qtl Mapping ◽  
Panicum Virgatum ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Pearson Correlation ◽  
Biomass Accumulation ◽  
Phenotypic Traits ◽  
Winter Dormancy ◽  
Phenotypic Data

Abstract Background Switchgrass (Panicum virgatum) undergoes seasonal growth changes based on the perception of photoperiod and temperature. It transitions to winter dormancy in early fall, breaks dormancy in the spring, and resume the cycle starting from flowering which is the cue for senescence. The length of growing season can impact the biomass accumulation and yield. In this study, we conducted QTL mapping of winter dormancy measured by fall regrowth height (FRH) and normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two bi-parental pseudo-F1 populations derived from crosses between the lowland AP13 with the lowland B6 (AB) with 285 progenies, and the lowland B6 with the upland VS16 (BV) with 227 progenies. Methods Pearson correlation coefficient between phenotypic traits was calculated to determine if some traits can be as surrogates for other traits. Broad-sense heritabilities were calculated to determine the inheritance, and QTL mapping was conducted for two-years phenotypic data to identify QTLs associated with the trais. Results We identified 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD. The ranges of percent variance explained by these QTLs varied between 4.21–23.27% for FRH, 4.47–24.06% for NDVI, 4.35–32.77% for SE, and 4.61–29.74% for FD. A higher number of QTL was discovered in the BV population, suggesting more variants in the lowland x upland population contributing to the expression of seasonal dormancy underlying traits. We identified 9 regions of colocalized QTL with possible pleiotropic gene action. The positive correlation between FRH or NDVI with dry biomass weight suggests that winter dormancy level could affect switchgrass biomass yield. The medium to high heritability levels of FRH (0.55–0.64 H2) and NDVI (0.30–0.61 H2) support the possibility of using the traits for indirect selection for biomass yield. Conclusion Markers found within the significant QTL interval can serve as genomic resource for breeding non-dormant and semi-dormant switchgrass cultivars for the southern regions, where growers can benefit from the longer production season.

scholarly journals The relationship between the normalized difference vegetation index, rainfall, and potential evapotranspiration in a banana plantation of Venezuela

2021 ◽  
Vol 18 (1) ◽  
pp. 58
Author(s):  
Barlin Orlando Olivares Campos ◽  
Franklin Paredes ◽  
Juan Carlos Rey ◽  
Deyanira Lobo ◽  
Stephanie Galvis-Causil
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Potential Evapotranspiration ◽  
Biomass Accumulation ◽  
Climatic Conditions ◽  
Vegetation Growth ◽  
Index Rainfall ◽  
Banana Wilt ◽  
The Relationship ◽  
Banana Plantation

<p>The water supply for rainfed crops such as bananas in the Aragua state of Venezuela is often uncertain, particularly towards the beginning of the rainy season (April-May). Where climatic conditions are seasonal, the temporal evolution of the NDVI (Normalized Difference Vegetation Index) closely accompanies the interannual variation of vegetation growth in response to thermal and hydric factors. The aim of the study is to assess the relationship between NDVI, rainfall and potential evapotranspiration during the period of January/2016 to December/2017 in a Venezuelan banana plantation. In this study, the NDVI derived from the GIMMS MODIS Terra product, the daily accumulated precipitation data (mm) and the daily mean air temperature (°C) were used as the only way to estimate the potential evapotranspiration. The results showed that the GMOD09Q1-based NDVI reflects reasonably well the spatiotemporal variation in biomass accumulation. Besides, this provides information on the water stress conditions in banana plants at the plot level. The influence of Precipitation and potential evapotranspiration on the NDVI was more evident when a lag of 1 month was considered in terms of the Spearman r, implying that there is a delay in the banana phonological response to rainfall changes and dryness conditions.  However, due to its low spatial resolution (i.e. 250 m), it is not adequate for the identification of banana wilt disease. Therefore, future studies are needed to assess other satellite-derived spectral indices for monitoring the health of banana plants over different sites in Venezuela.</p>

scholarly journals EXPLOITATION OF SENTINEL-2 IMAGES FOR LONG-TERM VEGETATION MONITORING AT A FORMER ORE PROCESSING SITE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1533-1537
Author(s):  
S. Fabre ◽  
A. Elger ◽  
T. Riviere
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Environmental Changes ◽  
Biomass Accumulation ◽  
Growth Patterns ◽  
Absolute Difference ◽  
Vegetation Monitoring ◽  
Ore Processing ◽  
Processing Site ◽  
Sentinel 2

Abstract. Excess metals in the soil or in plant tissues tend to have negative effects on plant health, growth, and biomass accumulation. The search for stressed or unusual growth patterns in cover vegetation has been enhanced by the use of vegetation index in the context of excessive exposure to heavy metals in the soil. This study aims to improve the monitoring of phyto-stabilized and natural vegetation of an ore processing site for several years after its closure by using multiple Sentinel-2 images. The time series is made up of 13 images, one image per season for four years. NDVI (Normalized Difference Vegetation Index), the most widely known and used vegetation index in the scientific literature, is used in combination with other spectral indexes identifying built-up areas and bare soils in order to enhance vegetation. A change detection technique based on absolute difference of vegetation maps is applied to detect abrupt changes related to meteorological conditions and significant environmental changes.

scholarly journals Normalized difference vegetation index obtained by ground-based remote sensing to characterize vine cycle in Rio Grande do Sul, Brazil

2017 ◽  
Vol 41 (5) ◽  
pp. 543-553 ◽  
Author(s):  
Amanda Heemann Junges ◽  
Denise Cybis Fontana ◽  
Rafael Anzanello¹ ◽  
Carolina Bremm
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Management Practices ◽  
Normalized Difference Vegetation Index ◽  
Rio Grande ◽  
Biomass Accumulation ◽  
Cabernet Sauvignon ◽  
Rio Grande Do Sul ◽  
Training Systems ◽  
Single Data

ABSTRACT The normalized difference vegetation index (NDVI) obtained by remote sensing is widely used to monitor annual crops but few studies have investigated its use in perennial fruit crops. The aim of this study was to determine the temporal NDVI profile during grapevine cycle in vineyards established in horizontal training systems. NDVI data were obtained by the ground-based remote sensing Greenseeker in Chardonnay and Cabernet Sauvignon vineyards located in the Serra Gaúcha region, Rio Grande do Sul, Brazil, from September to June in the 2014/2015 and 2015/2016 vegetative seasons. The grapevine canopies were managed in horizontal training systems (T-trellis and Y-trellis). The results indicated that the temporal NDVI values varied during the grapevine cycle (0.33 to 0.85), reflecting the changing in vigor and biomass accumulation that resulted from the phenological stages and management practices. The temporal NDVI profiles were similar to both horizontal training systems. The NDVI values were higher throughout the cycle for Cabernet Sauvignon compared to Chardonnay indicating Cabernet Sauvignon as the cultivar with greater vegetative vigor. The NDVI obtained by ground-based remote sensing is a fast and non-destructive tool to monitor and characterize the canopy in real time, compiling into a single data several parameters related to vine development, like meteorological conditions and management practices that are difficult to be quantified together.

scholarly journals ULTRA-HIGH SPATIAL RESOLUTION UAV-BASED IMAGERY TO PREDICT BIOMASS IN TEMPERATE GRASSLANDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 443-447 ◽  
Author(s):  
U. Lussem ◽  
A. Bolten ◽  
J. Menne ◽  
M. L. Gnyp ◽  
G. Bareth
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Low Cost ◽  
Rapid Assessment ◽  
Vegetation Indices ◽  
Multispectral Data ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Cost Efficient

<p><strong>Abstract.</strong> Monitoring biomass yield in grassland is of key importance to support sustainable management decisions. Especially the high spatio-temporal variety in grasslands requires rapid and cost-efficient data acquisition with a high spatial and temporal resolution. Therefore, this study aims to evaluate the comparability of UAV-based simultaneously acquired vegetation indices from a consumer-grade RGB-camera (Sony Alpha 6000) and a well-calibrated narrow-band multispectral camera (MicaSense RedEdge-M) to estimate dry matter biomass yield. The study site is an experimental grassland field in Germany with four nitrogen fertilizer levels. Biomass yield and UAV-based data for the first cut in May 2018 was analysed in this study. From the RGB-data the Plant Pigment Ratio Index (PPR) and the Normalized Green Red Difference Index (NGRDI) and from the multispectral data the Normalized Difference Vegetation Index (NDVI) are calculated as predictors for dry biomass yield. The NGRDI and NDVI perform moderately well with cross-validation R<sup>2</sup> of 0.57 and 0.63 respectively, while the PPR performs better with an R<sup>2</sup> of 0.70. These results indicate the potential of low-cost UAV-based methods for rapid assessment of grasslands.</p>

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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