Genome-Wide Association Study of Leaf Chlorophyll Content Using High-Density SNP Array in Peanuts (Arachis hypogaea L.)

The content of chlorophyll, a fundamental component required for photosynthesis in plants, has been widely studied across crop species. In this study, we aimed to evaluate the genetic diversity of 453 peanut accessions. We evaluated the evolutionary relationships using a genome-wide association study (GWAS) of leaf color data based on chlorophyll content analysis using the Axiom_Arachis array containing 58K single-nucleotide polymorphisms (SNPs). We identified seven SNPs as being significantly associated with leaf chlorophyll content on the chromosomes Aradu.A02, Aradu.A08, Araip.B02, Araip.B05, Araip.B06, and Araip.B08 in a GAPIT analysis. The SNP AX-176820297 on Araip.B05 was significantly linked with leaf chlorophyll content across the seasons. The Arahy.SDG4EV gene was detected to be in linkage disequilibrium (LD) with the significant SNPs, and its expression was significantly correlated with leaf chlorophyll content. The results of the current study provide useful and fundamental information with which to assess genetic variations in chlorophyll content and can be utilized for further genetic and genomic studies and breeding programs in peanuts.

Kaolin application to increase lettuce (Lactuca sativa) growth under suboptimal watering condition

Lettuce is a crop sensitive to water availability as it needs more water in cultivation. Kaolin has been reported to reduce the impact of drought stress in plants. However, the use of kaolin to increase lettuce growth under drought condition has not been reported yet. In this study, the effect of kaolin to increase the growth of lettuce under suboptimal watering condition was investigated. Kaolin foliar application (3%, 5%, and 6%) was tested on a group consisting of 5 lettuce plants grown under reduced watering condition in a greenhouse. Lettuce height, number of leaves, root length, leaf length and width, fresh and dry weight of lettuce, and leaf chlorophyll content were observed. Kaolin applications significantly increased the growth of lettuce in most parameters compared to the negative control (reduced watering condition, without kaolin). Kaolin 3% application presented the best treatment to increase growth parameters. All kaolin applications did not affect chlorophyll content significantly under lower watering condition. In conclusion, kaolin was able to reduce the impact of reduced watering condition and increase lettuce growth but showed no effect on the leaf chlorophyll content. Thus, further evaluation of kaolin application in lettuce under drought condition is needed.

QTL Analysis for Chlorophyll Content in Strawberry (Fargaria × ananassa Duch.) Leaves

Chlorophyll is an important factor facilitating plants to capture, allocate and transforms light energy and plays a major role in yield formation. Strawberry is one of the most important fruit crops worldwide. Breeding strawberry for better light utilization by improving photosynthetic efficiency can improve the yield potential. In strawberry, genetic studies have been done for several traits, but no reports on the genetic mapping of chlorophyll content in leaves. In the present study, we used two independent F2 mapping populations (BS-F2 and BC-F2) and, Axiom 35 K strawberry chip and genotyping-by-sequencing derived single nucleotide polymorphisms based linkage maps to identify the quantitative trait loci (QTLs) controlling leaf chlorophyll content. SPAD values were used to estimate the leaf chlorophyll content of parental lines and F2 populations. A total of seven QTLs, including major and minor effects, common and specific to populations, were identified across the strawberry genome explaining phenotypic variation (R2) ranging from 1.4 to 26.4%. Candidate genes associated with the photosynthesis and chlorophyll content were inferred in commonly detected QTLs. This work thus provides not only information for novel loci controlling chlorophyll content in strawberry leaves but also forms the basis for future marker assisted breeding in strawberry to select the plants for required chlorophyll content.

The Effect of LED Lighting on The Growth of Seedlings of Hybrid Tomato

A study was conducted to evaluate the effect of LED lighting on the growth of seedlings of hybrid tomato plants. Four hybrids (Captain F1, Refiner F1, Coral Reef F1 and Fire F1), seven treatments of irradiators (monochromatic red, green, blue, white) and three dual light treatments (green+blue, blue+red, green+red) were carried out in the laboratory of artificial climate. Radiation with monochromatic had a role in increasing hypocotyl length, the height of plant, transpiration and stomata connection. The results have shown a significant effect on radiation with monochromatic red on hypocotyl length (73.00mm) and the height of plant (30.94cm). Plants radiation with monochromatic blue gave the highest transpiration (4.69 mmol/m2.s) and stomatal conductance (0.30 mol./m2.s). The dual radiation had a role in increasing dry weight for the plant and leaf chlorophyll content (SPAD). Radiation with (green+red) provided the best stem dry weight (0.91g). Plants irradiated with (green+blue) exhibited the highest leaf dry weight and root dry weight (1.96g and 3.12g respectively). Plants radiation with (blue + red) showed the highest leaf chlorophyll content (SPAD) (559.19). In the monochromatic light, the effect of hybrid Fire F1 showed the highest hypocotyl length (64.11mm) and height of plant (29.00 cm), and hybrid Refind F1 had the highest transpiration (3.58 mmol/m2.s) and stomatal conductance (0.23 mol./m2.s). In the binary spectral effect, the hybrid Coral reef F1 resulted the highest stem dry weight, leaf dry weight and root dry weight (0.68 g,1.76 g and 2.39g respectively), and hybrid Refind F1 had the highest leaf chlorophyll content (SPAD) (468.27).

Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.

​​The Effects of Gibberellic Acid (GA3) Applications on the Physiological Features of Groundnut (Arachis hypogaea L.) under Different Salt (NaCI) Stress Conditions

Background: Salinity is one of the most imperative problems of farmers worldwide. Reports are available that the external application of GA3 can reduce harmful effects of salinity. Low concentration and small quantities of GA3, a phytohormone, is essential to hasten the growth and development of plants. The current study was aimed to test the efficiency of GA3 on the impart of salt tolerance in terms of better nutrient uptake under salt stress situation and to determine the effect of external giberellic acid applications on the physiological properties and proline level of peanuts under different salt (NaCl) stress conditions on the degree of resistance to salt stress in peanuts. Methods: Trial was established as split plot design with 3 replications and NC-7 variety was used as a material. The chlorophyll content in leaves, ion (K, Na, Mg, Ca) content, cell membrane permeability were searched in the research. To determine the physiological responses of groundnut in the conditions of Gibberellic asid (GA3), (0, 5, 15 mM) applications under the different salt (Nacl), (0, 25, 50, 100 mM) stress conditions at the Plant Growth Room in the GAP Agricultural Research Institute in Şanlıurfa, Turkey in 2019. The chlorophyll content in leaves, ion (K, Na, Mg, Ca) content, cell membrane permeability were searched in the research. Result: The increasing salt applications decreases the leaf chlorophyll content and leaf ions (K, Mg, Ca) content, in contrast, increased the cell membrane permeability, proline content and sodium concentration were determined. The gibberallic asid applied externally against salt stress were determined to have the positive effects on traits; leaf chlorophyll content, ion (K, Na, Mg, Ca) content, cell membrane permeability, proline content.

Effect of Variety and Planting Arrangement on Weed Control and Yield Performance of Transplanted Aman Rice

An experiment was conducted at the Agronomy Field Laboratory, Bangladesh Agricultural University, Mymensingh during June to November 2016 in order to find out the effect of variety and planting arrangement on weed control and yield performance of transplanted Aman rice. The experiment consisted of four varieties viz., Binadhan-7, Binadhan-12, Binadhan-17 and Binadhan-16 and five planting arrangement viz. 25cm × 20cm, 25cm × 15cm, 20cm × 20cm, 20cm × 15cm and 15cm × 15cm. The experiment was laid out in Split Plot Design with three replications. Results indicated that the highest weed density (90.93 m-2), weed biomass (131.87 gm-2), leaf chlorophyll content (SPAD value) (40.02), sterile spikelets panicle-1 (76.86) ,% sterility panicle-1 (28.66), grain yield (4.51 t ha-1) and straw yield (4.63 t ha-1) were obtained from Binadhan-17. The variety Binadhan-7 showed the best performance in respect of number of total tillers hill-1 (11.06), number of effective tillers hill-1 (10.12) compared to the other varieties used in the study. The highest leaf chlorophyll content (SPAD value) (36.48), plant height (99.72 cm), panicle length (22.03 cm), number of total tillers hill-1 (10.21) and number of effective tillers hill-1 (9.45) were found at 25 cm × 20 cm planting arrangement. The highest grain yield (4.31 t ha-1) was recorded from 20cm × 15cm planting arrangement. The interaction between variety and planting arrangement showed numerically higher grain yield (5.38 t ha-1) from Binadhan-7 at 20cm × 15cm planting arrangement, whereas the lowest grain yield (2.61 t ha-1) was achieved from the variety Binadhan-12 at 15cm × 15cm planting arrangement. It can be concluded that Binadhan-7 at 20cm × 15cm planting arrangement may be used for achieving the best yield with less weed interference in Aman season.

Using Spectral Reflectance to Estimate the Leaf Chlorophyll Content of Maize Inoculated With Arbuscular Mycorrhizal Fungi Under Water Stress

Leaf chlorophyll content is an important indicator of the growth and photosynthesis of maize under water stress. The promotion of maize physiological growth by (AMF) has been studied. However, studies of the effects of AMF on the leaf chlorophyll content of maize under water stress as observed through spectral information are rare. In this study, a pot experiment was carried out to spectrally estimate the leaf chlorophyll content of maize subjected to different durations (20, 35, and 55 days); degrees of water stress (75%, 55% and 35% water supply) and two inoculation treatments (inoculation with Funneliformis mosseae and no inoculation). Three machine learning algorithms, including the back propagation (BP) method, least square support vector machine (LSSVM) and random forest (RF) method, were used to estimate the leaf chlorophyll content of maize. The results showed that AMF increased the leaf chlorophyll content, net photosynthetic rate (A), stomatal conductance (gs), transpiration rate (E), and water use efficiency (WUE) of maize but decreased the intercellular carbon dioxide concentration (Ci) of maize and atmospheric vapor pressure deficit (VPD) regardless of the water stress duration and degree. The first-order differential spectral data can better reflect the correlation between leaf chlorophyll content and spectrum of inoculated maize when compared with original spectral data. The BP model performed bestin modeling the maize leaf chlorophyll content, yielding the largest R2-values and smallest root mean square error (RMSE) values, regardless of stress duration. These results provide a reliable basis for the effective monitoring of the leaf chlorophyll content of maize under water stress.