scholarly journals Increase of Yield, Lycopene, and Lutein Content in Tomatoes Grown Under Continuous PAR Spectrum LED Lighting

2021 ◽  
Vol 12 ◽  
Author(s):  
Dennis Dannehl ◽  
Thomas Schwend ◽  
Daniel Veit ◽  
Uwe Schmidt
Keyword(s):  
Emission Spectrum ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Visible Spectrum ◽  
Growth And Yield ◽  
Carotene Content ◽  
Light Emitting ◽  
Efficient Alternative ◽  
Number Of Leaves ◽  
Use Efficiency

Light emitting diodes (LEDs) are an energy efficient alternative to high-pressure sodium (HPS) lighting in tomato cultivation. In the past years, we have learned a lot about the effect of red and blue LEDs on plant growth and yield of tomatoes. From previous studies, we know that plants absorb and utilize most of the visible spectrum for photosynthesis. This part of the spectrum is referred to as the photosynthetically active radiation (PAR). We designed a LED fixture with an emission spectrum that partially matches the range of 400 to 700 nm and thus partially covers the absorption spectrum of photosynthetic pigments in tomato leaves. Tomato plants grown under this fixture were significantly taller and produced a higher fruit yield (14%) than plants grown under HPS lighting. There was no difference in the number of leaves and trusses, leaf area, stem diameter, the electron transport rate, and the normalized difference vegetation index. Lycopene and lutein contents in tomatoes were 18% and 142% higher when they were exposed to the LED fixture. However, the ß-carotene content was not different between the light treatments. Transpiration rate under LED was significantly lower (40%), while the light use efficiency (LUE) was significantly higher (19%) compared to HPS lighting. These data show that an LED fixture with an emission spectrum covering the entire PAR range can improve LUE, yields, and content of secondary metabolites in tomatoes compared to HPS lighting.

scholarly journals Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

2020 ◽  
Vol 13 (1) ◽  
pp. 165
Author(s):  
Hillary M. O. Otieno ◽  
George N. Chemining’wa ◽  
Shamie Zingore
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nutrient Management ◽  
Fertilizer Application ◽  
Growth And Yield ◽  
Yield Prediction ◽  
Maize Productivity ◽  
Dry Biomass ◽  
The Right

To mitigate low maize productivity, improve on-farm planning and policy implementation, the right fertilizer combinations and yield forecasting should be prioritized. Therefore, this research aimed at assessing the effect of applying different nutrient combinations on maize growth and yield and in-season grain yield prediction from biomass and normalized difference vegetation index (NDVI) readings. The research was done in Embu and Kirinyaga counties, in Central Kenya. Nutrient combinations tested were P+K, N+K, N+P, N+P+K, and N+P+K+Ca+Mg+Zn+B+S. The results showed consistently lowest and highest NDVI reading, dry biomass, and grain yields due to P+K and N+P+K+Ca+Mg+Zn+B+S treatments, respectively. Positive NDVI responses of 56%, 14%, 15%, and 15% were recorded with N, P, K, and combined Ca+Mg+Zn+B+S, respectively. These nutrients, in the same order, recorded 54%, 20%, 8%, and 18% positive responses with biomass. The GreenSeeker NDVI reading with grain yield and aboveground dry biomass with grain yield recorded R2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga), respectively. When data were pooled, the prediction strength increased, reaching a maximum of 67% and 58% with NDVI and biomass, respectively. Yield prediction was even more robust when the independent variables were combined through multiple linear model at both 85 and 105 days after emergence. From this research, it is evident that the effects of balanced fertilizer application are detectable from NDVI readings—providing a tool for tracking and monitoring nutrient management effects—not just from the nitrogen perspective as commonly studied but from the combined effects of multiple nutrients. Also, grain yield could be accurately predicted early before harvesting by combining NDVI and biomass yields.

scholarly journals Identification of the QTL-allele System Underlying Two High-Throughput Physiological Traits in the Chinese Soybean Germplasm Population

2021 ◽  
Vol 12 ◽  
Author(s):  
Lei Wang ◽  
Fangdong Liu ◽  
Xiaoshuai Hao ◽  
Wubin Wang ◽  
Guangnan Xing ◽  
...  
Keyword(s):  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Genome Wide Association Study ◽  
Agronomic Traits ◽  
Block Design ◽  
Physiological Traits ◽  
Growth And Yield ◽  
Genetic Changes ◽  
Soybean Germplasm

The QTL-allele system underlying two spectral reflectance physiological traits, NDVI (normalized difference vegetation index) and CHL (chlorophyll index), related to plant growth and yield was studied in the Chinese soybean germplasm population (CSGP), which consisted of 341 wild accessions (WA), farmer landraces (LR), and released cultivars (RC). Samples were evaluated in the Photosynthetic System II imaging platform at Nanjing Agricultural University. The NDVI and CHL data were obtained from hyperspectral reflectance images in a randomized incomplete block design experiment with two replicates. The NDVI and CHL ranged from 0.05–0.18 and 1.20–4.78, had averages of 0.11 and 3.57, and had heritabilities of 78.3% and 69.2%, respectively; the values of NDVI and CHL were both significantly higher in LR and RC than in WA. Using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) method, 38 and 32 QTLs with 89 and 82 alleles and 2–4 and 2–6 alleles per locus were identified for NDVI and CHL, respectively, which explained 48.36% and 51.35% of the phenotypic variation for NDVI and CHL, respectively. The QTL-allele matrices were established and separated into WA, LR, and RC submatrices. From WA to LR + RC, 4 alleles and 2 new loci emerged, and 1 allele was excluded for NDVI, whereas 6 alleles emerged, and no alleles were excluded, in LR + RC for CHL. Recombination was the major motivation of evolutionary differences. For NDVI and CHL, 39 and 32 candidate genes were annotated and assigned to GO groups, respectively, indicating a complex gene network. The NDVI and CHL were upstream traits that were relatively conservative in their genetic changes compared with those of downstream agronomic traits. High-throughput phenotyping integrated with RTM-GWAS provides an efficient procedure for studying the population genetics of traits.

scholarly journals Evaluation of Organic Camelina Crop Under Different Tillage Systems and Fertilization Types Using Proximal Remote Sensing

2020 ◽  
Vol 77 (1) ◽  
pp. 1
Author(s):  
Foteini ANGELOPOULOU ◽  
Evangelos ANASTASIOU ◽  
Spyros FOUNTAS ◽  
Dimitrios BILALIS
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth And Yield ◽  
Tillage Systems ◽  
Minimum Tillage ◽  
Spectral Vegetation Indices ◽  
Crop Parameters

A field experiment was conducted in Southern Greece to assess Normalized Difference Vegetation Index (NDVI) and Red-Edge Normalized Difference Vegetation Index (NDRE) in estimating Camelina’s crop growth and yield parameters under different tillage systems (conventional and minimum tillage) and organic fertilization types (compost, vermicompost and untreated control). A proximal canopy sensor was used to measure the aforementioned Spectral Vegetation Indices (SVIs) at different days after sowing (DAS). Camelina presented the highest values of NDVI and NDRE under compost fertilization (0.63 and 0.22 accordingly) and minimum tillage system (0.50 and 0.18 accordingly). Additionally, the highest correlations between the measured crop parameters and NDVI, NDRE were achieved at leaf development to early flowering stage. Moreover, NDRE presented the highest correlation with seed yield (R2=0.60, p<0.05) and thus it is suggested for estimating Camelina’s productivity instead of NDVI. Finally, further research is needed for adopting the use of remote sensing technologies on predicting Camelina’s crop growth and yield.

scholarly journals Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

2020 ◽  
Vol 12 (18) ◽  
pp. 2970
Author(s):  
Anna C. Talucci ◽  
Elena Forbath ◽  
Heather Kropp ◽  
Heather D. Alexander ◽  
Jennie DeMarco ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Visible Spectrum ◽  
Basal Area ◽  
Vegetation Recovery ◽  
Climate Conditions ◽  
Northeastern Siberia ◽  
Larch Forests

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.

Estimating canola (Brassica napus L.) yield potential using an active optical sensor

2009 ◽  
Vol 89 (6) ◽  
pp. 1149-1160 ◽  
Author(s):  
C B Holzapfel ◽  
G P Lafond ◽  
S A Brandt ◽  
P R Bullock ◽  
R B Irvine ◽  
...  
Keyword(s):  
Seed Yield ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Yield Potential ◽  
Regression Analyses ◽  
Canola Seed ◽  
Soil Zone ◽  
Use Efficiency

Active optical sensors have potential as tools to increase N fertilizer use efficiency in crop production; however, empirical data are required to utilize the sensors for this purpose. Data were compiled from N fertilizer trials at five Canadian locations (2004-2007) to determine the feasibility of using optical sensors during the growing season to estimate the seed yield potential of canola (Brassica napus). The normalized difference vegetation index (NDVI) of each plot in each trial was measured using a hand-held optical sensor several times each season. The NDVI between the six-leaf stage and the beginning of flowering was divided by one of several different heat unit summations to normalize the measurements, and data were combined across locations. Linear and exponential regression analyses were completed for canola seed yield as a function of both the original and normalized NDVI measurements. When data from all locations were combined, NDVI was significantly correlated with canola seed yield (R2 = 0.35; P < 0.001) and normalizing NDVI did not improve the correlation. Categorizing the locations by soil zone (Brown-Dark Brown and thin-Black-Black) and completing separate regression analyses for each group increased the correlation coefficients for NDVI and seed yield (R2 = 0.36-0.43). Furthermore, dividing NDVI by the heat unit summations generally improved the correlation when the data were categorized by soil zone. The largest correlation coefficient occurred when NDVI was divided by growing degree days with a base temperature of 5°C (R2 = 0.53-0.67). Our results show that optical sensors can be used to estimate canola yield potential early enough in the growing season to have potential as an N management tool.Key words: Normalized difference vegetation index, agriculture, precision, nitrogen use efficiency

scholarly journals Characterizing Variation in Nitrogen Use Efficiency in Wheat Genotypes Using Proximal Canopy Sensing for Sustainable Wheat Production

Agronomy ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 773 ◽  
Author(s):  
Mohammed A. Naser ◽  
Raj Khosla ◽  
Louis Longchamps ◽  
Subash Dahal
Keyword(s):  
Nitrogen Use Efficiency ◽  
Growth Stage ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Strong Relationship ◽  
Triticum Aestivum L ◽  
Nitrogen Use ◽  
Wheat Genotypes ◽  
Use Efficiency ◽  
Partial Factor Productivity

Global nitrogen use efficiency (NUE) for cereal production is marginal and is estimated to be about 33%. Remote sensing tools have tremendous potential for improving NUE in crops through efficient nitrogen management as well as the identification of high-NUE genotypes. The objectives of this study were (i) to identify and quantify the variation in NUE across 24 winter wheat genotypes (Triticum aestivum L.) and (ii) to determine if the normalized difference vegetation index (NDVI) could characterize the variability in NUE across wheat genotypes. This study was conducted in 2010 and 2011 in the semi-arid climate of Northeastern Colorado across dryland and irrigated conditions. Our results indicate significant variation in the NUE among genotypes across two irrigation conditions. We observed a strong relationship between the NDVI and NUE—as PFP (partial factor productivity) and PNB (partial nitrogen balance)—across the 24 wheat genotypes under dryland conditions (average R2 for PFP and PNB = 0.84) at Feekes growth stage 11.1, for site year II. However, poor association was observed under irrigated conditions (average R2 for PFP and PNB = 0.29) at Feekes growth stage 3 to 4 for site year II. This study demonstrates the potential and limitations of active canopy sensing to successfully characterize the variability in NUE across wheat genotypes.

scholarly journals Yield and nutritional efficiency of corn in response to rates and splits of nitrogen fertilization

Revista CERES ◽  
2017 ◽  
Vol 64 (4) ◽  
pp. 351-359
Author(s):  
Telmo Jorge Carneiro Amado ◽  
Enrique Oswin Hahn Villalba ◽  
Rafael Pivotto Bortolotto ◽  
Douglas Dalla Nora ◽  
Jardes Bragagnolo ◽  
...  
Keyword(s):  
Nitrogen Fertilization ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Block Design ◽  
N Fertilization ◽  
Total N ◽  
Randomized Block Design ◽  
Use Efficiency ◽  
Corn Plants ◽  
The Moment

ABSTRACT Despite its relevance, nitrogen is poorly utilized by the plants when improperly applied. Thus, the objective of this study was to evaluate the yield and nitrogen use efficiency (NUE) in corn in response to doses and split application of nitrogen fertilization. The experimental design was a randomized block design, with three replications. Doses of nitrogen of 0, 30, 60 and 180 kg ha-1 were applied at sowing in order to create different nutritional status of corn plants and to obtain different values of Normalized Difference Vegetation Index (NDVI) measured with “Greenseeker®” optical sensor. The subplots with nitrogen doses in topdressing of 0, 30, 60 and 90 kg ha-1 at V8 and a dose of 60 kg ha-1 at V12 were placed in experimental plots with doses of 0, 30, 60 and 180 kg ha-1 of nitrogen at sowing. Moreover, NUE was calculated in the experiment using agronomic indexes determined by applications of nitrogen in late topdressing (V8 and V12) and contrasted to the possible combinations at doses of 60, 90 and 120 kg ha-1 of total N applied. The results showed the occurrence of a linear relationship between nitrogen fertilizer dose and NDVI at V8 as well as at V12 stages. Late topdressing fertilizations (V12) did not cause a decrease in grain yield when combined with nitrogen fertilization at sowing, moreover resulted in higher NUE. Split the nitrogen dose showed better NUE than the combinations where nitrogen was not applied at sowing or in topdressing. The delay of nitrogen topdressing can be an alternative for the planning of the moment of the N fertilization according to the climate forecast in each region.

Performance of lettuce plants under organic mulch and LEDs light colors

2021 ◽  
Vol 7 (3) ◽  
pp. 126-141
Author(s):  
Ihab I Sadek ◽  
Fatma S Moursy ◽  
Tarek M Younis
Keyword(s):  
Bare Soil ◽  
Growth And Yield ◽  
Natural Light ◽  
Positive Role ◽  
Light Emitting ◽  
Organic Mulch ◽  
Different Types ◽  
Number Of Leaves ◽  
Plot Design

This study was performed out at net house, privet farm, Cairo-Alexandria desert road, 80 Km, to present the positive role of using different types of organic mulch; different LEDS (light-emitting diodes) light colors and their combination on lettuce plants as growth and yield. Three types of organic mulch i.e., (mushroom wastes, compost and palm fibers) compared to bare soil and four LEDs light colors i.e., (white, yellow, green and "red + blue + green") plus natural light. Seedlings of lettuce cv. Iceberg were transplanting at 1st November through 2019 and 2020seasons. The study was conducted in a split plot design with three replications. Results obtained that using different types of organic mulch, different LEDs light colors and their combination had a significant overall tested parameters (plant length, number of leaves/head, fresh and dry weights of leaves, leaves contents from N, P and K and total heads yield/m2). In general, cultivated lettuce plants with using different types organic mulch and different LEDs light colors enhanced all tested parameters compared to bare soil or/and natural light. The most positive role of tested factors was noticed with using compost mulch, LEDs "R + B+ G" colors and their combination as compost mulch plus LEDs "R + B+ G colors", which, had greatest values of all tested parameters more than other treatments.

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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