Measurements Of Chlorophyll Content Research Articles

The fitness of pelargonium cuttings affects the relationship between the photochemical activity of the photosynthetic apparatus and rooting ability

Pelargoniums cultivated for ornamental purposes rely on efficient vegetative propagation. This study researched applicability of chlorophyll fluorescence for validating the physiological conditions of pelargonium cuttings. Results indicated a correlation between the chlorophyll fluorescence and rooting potential. The ET0/RC values were negatively correlated with the rooting efficiency between the varieties and the duration of cold storage. A negative correlation was observed between OJIP parameters, representing energy flow in thylakoids, and chlorophyll content in cuttings with lower nutritional status. The phenomenological energy fluxes for leaf cross-sections and the number of active PSII reaction centers in the not-excited state (RC/CS0) increase with raised chlorophyll concentration. This imply the influence of rooting ability on the demand for photoassimilates in pelargonium cuttings, which can be detected early on through chlorophyll fluorescence analysis but not chlorophyll content measurements. Chlorophyll fluorescence evaluation, along with specific OJIP test parameters such as the performance indices PIABS and PItotal, prove useful for predicting rooting efficiency in relation to the nutritional status of cuttings, suggesting the effects of cuttings cold storage and discerning varietal differences in rooting. This study establishes the pragmatic application of chlorophyll fluorescence assessment for elucidating the physiological intricacies of pelargonium cuttings and factors influencing rooting efficiency.

Evaluation of the Reliability of the CCM-300 Chlorophyll Content Meter in Measuring Chlorophyll Content for Various Plant Functional Types.

Chlorophyll fluorescence is a well-established method to estimate chlorophyll content in leaves. A popular fluorescence-based meter, the Opti-Sciences CCM-300 Chlorophyll Content Meter (CCM-300), utilizes the fluorescence ratio F735/F700 and equations derived from experiments using broadleaf species to provide a direct, rapid estimate of chlorophyll content used for many applications. We sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use. We linked CCM-300 measurements of broadleaf, conifer, and graminoid samples in 2018 and 2019 to high-performance liquid chromatography (HPLC) and/or spectrophotometric (Spec) analysis of the same leaves. We observed a significant difference between the CCM-300 and HPLC/Spec, but not between HPLC and Spec. In comparison to HPLC, the CCM-300 performed better for broadleaves (r = 0.55, RMSE = 154.76) than conifers (r = 0.52, RMSE = 171.16) and graminoids (r = 0.32, RMSE = 127.12). We observed a slight deterioration in meter performance between years, potentially due to meter calibration. Our results show that the CCM-300 is reliable to demonstrate coarse variations in chlorophyll but may be limited for cross-plant functional type studies and comparisons across years.

Phycoremediation of Potato Industry Wastewater for Nutrient Recovery, Pollution Reduction, and Biofertilizer Production for Greenhouse Cultivation of Lettuce and Celery in Sandy Soils

Microalgae-based wastewater treatment offers an eco-friendly opportunity for simultaneous nutrient recovery and biomass generation, aligning with the circular bioeconomy concept. This approach aims to utilize the nutrients of potato industry wastewater (PIW) for algal growth while mitigating the environmental impact of this industrial byproduct. This study focused on cultivating three cyanobacterial strains, Anabaena oryzae, Nostoc muscorum, and Spirulina platensis, in PIW and synthetic media for 30 days to assess feasibility. Growth performance was monitored by measuring chlorophyll content, dry weight (DW), optical density (OD), and pH at 3-day intervals. The high-performing cyanobacterial biomass from the laboratory findings was formulated into a biofertilizer, which was then evaluated in a controlled greenhouse experiment on celery and lettuce plants. The biofertilizer replaced conventional NPK mineral fertilizers at different levels (25%, 50%, and 75%), while a control group received 100% chemical fertilizer. The results showed favourable growth of all three cyanobacteria strains and their mixture in PIW throughout the experiment. The mixed cyanobacteria followed by Spirulina platensis exhibited the highest growth rates, achieving chlorophyll contents of 3.75 and 2.30 µg·mL−1, DWs of 1.79 g·L−1 and 1.63 g·L−1, and ODs of 0.41 and 0.38, respectively, surpassing the other treatments. The formulated biofertilizers, Spi-PIW (Spirulina platensis + potato industry wastewater) and Cyano-PIW (mixed culture+ potato industry wastewater), significantly enhanced plant height, root and stem lengths, and the number of leaves per plant in celery and lettuce compared to the control group. These biofertilizer treatments also improved chlorophyll contents, as well as macro- and micronutrient levels, in the two crops. Additionally, the application of these biofertilizers improved certain sandy soil properties, i.e., pH, total organic matter, total nitrogen, phosphorus, and potassium. In conclusion, utilizing PIW as a substrate for cultivating cyanobacteria strains and producing high-quality liquid bio-organic fertilizers holds potential for reducing recommended NPK fertilizer doses by 25–50% in celery and lettuce growth, providing an environmentally friendly approach.

Physiological and anatomical responses of Passiflora tripartita var. mollissima (Passifloraceae) in water deficit

Introduction: Passiflora tripartita var. mollissima (banana passionfruit) is one of the most promising exotic tropical fruits from the diversity of the Passifloraceae family in South America, because of its organoleptic properties and antioxidant activity. Objective: To evaluate the physiological and anatomical responses of banana passionfruit plants under water deficit to better understand the mechanisms that mitigate this stress and affect the production of crops subject to climate change and global warming. Methods: Three-month-old seedlings of banana passionfruit were subjected to a soil water deficit through an irrigation reduction at 70 % for 49 days under greenhouse conditions. Morphology (leaf area, height, and number of leaves) and physiological (stomatal conductance, Fv/Fm, total chlorophyll content) measurements were made through time, and after the irrigation treatments were measured biomass parameters and anatomical foliar traits. Results: The plants experienced a decrease in height, leaf area, number of leaves, leaf area index, and relative water content, that are common responses in plants subjected to reduced irrigation. Additionally, the plants exhibited certain mechanisms that can be attributed to water deficit tolerance such as higher root: shoot ratio, stomatal closing, an increase in stomatal density, a reduction in mesophyll tissue thickness, and a decrease in the number of vessels and its diameter as they enable the banana passionfruit to reduce water loss and decrease the probability of cavitation in xylem vessels. Conclusions: banana passionfruit plants could implement strategies against water scarcity, allowing them to survive and endure challenging environmental conditions

Fractionation and characterization of Dunaliella microalgal biomass and extraction of carotenoids

This paper presents findings on the application of two fractionation protocols of Dunaliella microalgal biomass with the aim to separate and recover the lipid, pigment and protein fraction, leaving as “residue” the fraction that contains carbohydrates. The first protocol (LPC) is based on the initial extraction of lipids, followed by extraction of proteins, while in the second protocol (CLP), the pigments are first extracted followed by lipids extraction. With the LPC protocol the recovered lipid fraction was 34 and 32 wt%, while the recovered proteinic fraction was 46 and 50 wt% for the two samples under study, i.e. 1120 (D. granulata) and 1220 (D. minutissima). The CLP protocol led to the initial extraction of the pigment fraction with a percentage of 10–15 wt% while the subsequently recovered lipidic fraction was 16–24 wt%. The initial biomass was characterized for its moisture (<0.7 wt% for both samples), ash (approx. 45 wt% for both samples), organic matter, elemental composition and higher heating value (HHV) which was 23 and 30 MJ/kg (organic matter based-OMB). The composition of the lipid fraction was analyzed via esterification and gas chromatography (GC-FID) showing C16 and C18 as the predominant acids in the samples. The residue/carbohydrate fraction was analyzed via acid hydrolysis and analysis by high pressure liquid chromatography (HPLC) showing about 20 wt% (-OMB) content of carbohydrates. The high ash content of the biomass was found to be co-extracted during the fractionation protocols at a percentage of ≈49 % in the lipidic fraction and 45 % in the proteinic fraction. Furthermore, emphasis was given on pigment extraction and recovery using various solvents and quantification methods. Measurements of β-carotene, carotenoids, and chlorophyll content through UV and HPLC analyses is presented with chlorophyl and carotenoid content being ≈12 wt% and ≈9 wt% for samples 1120 and 1220 (-OMB). Additionally, a comparison between three solvents (THF, dichloromethane, acetone) was performed regarding their ability to extract the pigments from the microalgal biomass showing that acetone is capable of extracting pigments when concentration of the pigments is not high. The comparison of the HPLC with the UV method, suggested that UV spectroscopy is a reliable and rapid alternative for pigments determination, while HPLC enables identification and quantitative determination of specific pigments and chlorophyll species. The study provides insights for different fractionation protocols applied in two not yet fully studied Dunaliella species as well as the chlorophyl and carotenoid content.

Cunninghamia lanceolata Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

This study aimed to streamline the determination of chlorophyll content in Cunninghamia lanceolate while achieving precise measurements of canopy chlorophyll content. Relative chlorophyll content (SPAD) in the Cunninghamia lanceolate canopy were assessed in the study area using the SPAD-502 portable chlorophyll meter, alongside spectral data collected via onboard multispectral imaging. And based on the unmanned aerial vehicle (UAV) multispectral collection of spectral values in the study area, 21 vegetation indices with significant correlation with Cunninghamia lanceolata canopy SPAD (CCS) were constructed as independent variables of the model’s various regression techniques, including partial least squares regression (PLSR), random forests (RF), and backpropagation neural networks (BPNN), which were employed to develop a SPAD inversion model. The BPNN-based model emerged as the best choice, exhibiting test dataset coefficients of determination (R2) at 0.812, root mean square error (RSME) at 2.607, and relative percent difference (RPD) at 1.942. While the model demonstrated consistent accuracy across different slope locations, generalization was lower for varying slope directions. By creating separate models for different slope directions, R2 went up to about 0.8, showcasing favorable terrain applicability. Therefore, constructing inverse models with different slope directions samples separately can estimate CCS more accurately.

Transcriptome-Based Weighted Gene Co-Expression Network Analysis Reveals the Photosynthesis Pathway and Hub Genes Involved in Promoting Tiller Growth under Repeated Drought–Rewatering Cycles in Perennial Ryegrass

Drought stress, which often occurs repeatedly across the world, can cause multiple and long-term effects on plant growth. However, the repeated drought–rewatering effects on plant growth remain uncertain. This study was conducted to determine the effects of drought–rewatering cycles on aboveground growth and explore the underlying mechanisms. Perennial ryegrass plants were subjected to three watering regimes: well-watered control (W), two cycles of drought–rewatering (D2R), and one cycle of drought–rewatering (D1R). The results indicated that the D2R treatment increased the tiller number by 40.9% and accumulated 28.3% more aboveground biomass compared with W; whereas the D1R treatment reduced the tiller number by 23.9% and biomass by 42.2% compared to the W treatment. A time-course transcriptome analysis was performed using crown tissues obtained from plants under D2R and W treatments at 14, 17, 30, and 33 days (d). A total number of 2272 differentially expressed genes (DEGs) were identified. In addition, an in-depth weighted gene co-expression network analysis (WGCNA) was carried out to investigate the relationship between RNA-seq data and tiller number. The results indicated that DEGs were enriched in photosynthesis-related pathways and were further supported by chlorophyll content measurements. Moreover, tiller-development-related hub genes were identified in the D2R treatment, including F-box/LRR-repeat MAX2 homolog (D3), homeobox-leucine zipper protein HOX12-like (HOX12), and putative laccase-17 (LAC17). The consistency of RNA-seq and qRT-PCR data were validated by high Pearson’s correlation coefficients ranging from 0.899 to 0.998. This study can provide a new irrigation management strategy that might increase plant biomass with less water consumption. In addition, candidate photosynthesis and hub genes in regulating tiller growth may provide new insights for drought-resistant breeding.

Biochemical changes in some banana cultivars infected by Banana bunchy top virus

Bunchy top is one of the major diseases on bananas that caused economic losses. This study aimed to determine changes in protein content, the concentration of phenolic compounds, the amount of chlorophyll, and sugar content in several Banana bunchy top virus (BBTV)-infected banana cultivars. The research involved the artificial inoculation, extraction, and biochemical analysis of banana seedlings from the cultivars Mas, Cavendish, Kepok, and Raja. Inoculation was performed using the vector Pentalonia sp.. A factorial complete randomized design (CRD) was used as the experimental design. Each experimental unit consisted of 5 repetitions. Protein, phenol, chlorophyll, and sugar content measurements were performed using a UV-Vis spectrophotometer. Test samples were in the form of fresh leaves from BBTV-infected and non-BBTV-infected plants. Regression analysis and tests for significance were performed to determine the biochemical changes that occurred after testing. The results showed that the average levels of phenols, chlorophyll, protein, and total sugars in BBTV-infected plants were significantly altered. The chlorophyll content decreased from 82–87% in Mas, Cavendish, and Raja cultivars. Total plant phenol in all BBTV-infected cultivars increased by 69.2 to 348.3 ppm. Specificity was evident in the change in protein concentration, which increased by 95 ppm and 84.5 ppm in the Mas and Cavendish cultivars, respectively. This contrasts with the Kepok and Raja cultivars, which decreased by 660.5 ppm and 113.6 ppm, respectively. Sugar levels increased from 3133.9 to 3298.6 ppm in all BBTV-infected breeds. According to the data, BBTV infection has been shown to cause physiological and biochemical changes in Mas, Cavendish, Kepok, and Raja.

Estimation of the Relative Chlorophyll Content of Carya illinoensis Leaves Using Fractional Order Derivative of Leaf and Canopy Scale Hyperspectral Data

Chlorophyll is a crucial physiological and biochemical indicator that impacts plant photosynthesis, accumulation of photosynthetic products, and final yield. The measurement and analysis of chlorophyll content in plants can provide valuable insights into their nutritional status and overall health. The non-destructive and efficient estimation of relevant plant physiological and biochemical indicators using hyperspectral technology can provide a reliable method for collecting data on nutrient levels and health status during plant growth and development. Fifty-three Carya illinoensis plants of Jiande and Changlin series known for their exceptional qualities and significant economic benefits were used as the research object for collecting their leaf and canopy hyperspectral data. Firstly, fractional order derivative (FOD) was used for spectral preprocessing. Secondly, the spectral response relationship between spectrum and relative chlorophyll content (soil and plant analyzer development, SPAD) was explored by combining single-band and two-band spectral index (normalized difference spectral index, NDSI). The correlation coefficient of Pearson correlation analysis was used to estimate the linear correlation between variables. Finally, the correlation between the spectral feature variables and SPAD was analyzed and calculated. Top 10 absolute values of the correlation coefficients were screened out as modeling variables. eXtreme gradient boosting (XGBoost) machine learning algorithm was used to construct the optimal estimation model of SPAD of Carya illinoensis leaves. Results showed that the correlation between leaf and canopy spectrum after FOD pretreatment and SPAD was substantially improved, compared with raw spectrum. FOD combined with leaf and canopy NDSI was more effective than single band in improving the correlation between spectral characteristics and target components, which was increased by 0.166 and 0.338, respectively. The leaf spectrum could estimate SPAD more accurately than that of canopy spectrum. The optimal SPAD model was the 0.5th-order derivative transformation combined with two-band leaf spectral index (NDSI) model. The R2 P was 0.788, and the RMSEP was 0.842 in prediction set. On one hand, this study confirms the feasibility of rapid and non-destructive estimation of SPAD of Carya illinoensis leaves using hyperspectral technology. On the other hand, FOD combined with two-band spectral indices can significantly improve the response relationship between spectral characteristics and target variables, enrich hyperspectral data processing methods, and propose a novel approach for the detection of plant nutrient level and health.

Investigating the impact of silencing an RNA-binding protein gene SlRBP1 on tomato photosynthesis through RNA-sequencing analysis

Photosynthesis in plants directly affects the synthesis and accumulation of organic matter, which directly influences crop yield. RNA-binding proteins (RBPs) are involved in the regulation of a variety of physiological functions in plants, while the functions of RBPs in photosynthesis have not been clearly elucidated. To investigate the effect of a glycine-rich RNA-binding protein (SlRBP1) in tomato on plant photosynthesis, a stably inherited SlRBP1 silenced plant in Alisa Craig was obtained by plant tissue culture using artificial small RNA interference. It turns out that the size of the tomato fruit was reduced and leaves significantly turned yellow. Chlorophyll(Chl) content measurement, Chl fluorescence imaging and chloroplast transmission electron microscopy revealed that the chloroplast morphology and structure of the leaves of tomato amiR-SlRBP1 silenced plants were disrupted, and the chlorophyll content was significantly reduced. Measurement of photosynthesis rate of wild-type and amiR-SlRBP1 silenced plants in the same period demonstrated that the photosynthetic rate of these plants was significantly reduced, and analysis of RNA-seq data indicated that silencing of SlRBP1 significantly reduced the expression of photosynthesis-related genes, such as PsaE, PsaL, and PsbY, and affected the yield of tomato fruits through photosynthesis.

Deep chlorophyll maximum in temperate lakes with different trophic conditions − a rare or common phenomenon?

Deep chlorophyll maxima (DCM) are a common phenomenon in low-trophic stratified lakes, and there is much less information about the occurrence of DCM in eutrophic and dystrophic lakes. Therefore, we performed in situ continuous measurements of chlorophyll concentration by a submersible spectrofluorometer in 23 temperate lakes with different trophic conditions (mesotrophic, eutrophic, and dystrophic). We perform chemical analyses of available nutrient forms to better understand the mechanism of DCM formation. We found the highest concentration of phytoplankton in the metalimnion or upper hypolimnion of most studied lakes, regardless of trophic conditions. Nevertheless, the differences in the number of phytoplankton between DCM and epilimnion were largest in low-trophic lakes, where the chl a concentration in DCM was even 15 times higher than that in the epilimnion. The mesotrophic lakes showed vertical niche segregation by phytoplankton, with a general pattern − green algae in the upper layer, below diatoms, then cryptophytes (and cyanobacteria). Our results indicated that the main factor for DCM in mesotrophic and eutrophic lakes was epilimnetic nutrient depletion, while in dystrophic lakes, it seems to be caused by other factors due to the depletion of dissolved nutrients in whole water profiles.

Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images

Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R2): 0.737–0.832, root mean square error (RMSE): 3.35–4.202 Dualex readings), Flav (R2: 0.321–0.461, RMSE: 0.13–0.145 Dualex readings), and NBI (R2: 0.758–0.797, RMSE: 2.922–3.229 Dualex readings). (3) The combination of the threshold method with NDVI &lt; 0.55 and NBI &lt; 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas.

Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration

Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220–1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.

HSI combined with CNN model detection of heavy metal Cu stress levels in apple rootstocks

In recent years, the prolonged application of copper-based fungicides, ripening agents, and fertilizers in agricultural activities has resulted in copper (Cu) levels in the soil exceeding safe levels. Excess Cu has a negative impact on plant growth and crop yields. The practice of grafting horticultural crops onto appropriate rootstocks is frequently employed as a preventive measure against copper accumulation and its associated toxicity. Therefore, the selection of high-quality rootstocks that are not contaminated with heavy metal Cu is important for the growth of grafted crops and the breeding of grafted crops. This study proposed a technique based on convolutional neural network (CNN) and hyperspectral imaging (HSI) for the rapid, non-destructive, and accurate identification of copper contamination levels in apple rootstocks. The effect of different heavy metal Cu contamination levels on leaves was obtained by measuring chlorophyll content in leaves using the ultraviolet spectrophotometer method. A hybrid strategy for spectral variable selection was utilized, involving competitive adaptive reweighted sampling and successive projection algorithms. A traditional partial least squares discriminant analysis linear classification model, and least squares support vector machine, random forest, and extreme learning machine nonlinear models were developed. The results showed that the accuracy of the CNN model and Macro-F1 were 99.6% and 99.2%, respectively, which were better than the traditional linear and nonlinear models. Principal component analysis and t-distributed stochastic neighbor embedding were used for visual analysis to investigate the clustering patterns among sample classes. The results indicated that the combination of CNN and HSI techniques has great potential for classifying copper contamination levels in apple rootstocks.

Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing

Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also are time-consuming, often resulting in obtaining monitoring results after the optimal growth period of crops. Unmanned aerial vehicle (UAV) remote sensing technology offers the potential for rapidly acquiring chlorophyll content estimations over large areas. Currently, most studies only utilize single features from UAV data and employ traditional machine learning algorithms to estimate chlorophyll content, while the potential of multisource feature fusion and stacking ensemble learning in chlorophyll content estimation research remains largely unexplored. Therefore, this study collected UAV spectral features, thermal features, structural features, as well as chlorophyll content data during maize jointing, trumpet, and big trumpet stages, creating a multisource feature dataset. Subsequently, chlorophyll content estimation models were built based on four machine learning algorithms, namely, ridge regression (RR), light gradient boosting machine (LightGBM), random forest regression (RFR), and stacking ensemble learning. The research results demonstrate that (1) the multisource feature fusion approach achieves higher estimation accuracy compared to the single-feature method, with R2 ranging from 0.699 to 0.754 and rRMSE ranging from 8.36% to 9.47%; and (2) the stacking ensemble learning outperforms traditional machine learning algorithms in chlorophyll content estimation accuracy, particularly when combined with multisource feature fusion, resulting in the best estimation results. In summary, this study proves the effective improvement in chlorophyll content estimation accuracy through multisource feature fusion and stacking ensemble learning. The combination of these methods provides reliable estimation of chlorophyll content using UAV remote sensing technology and brings new insights to precision agriculture management in this field.

Genetic diversity of Ethiopian durum wheat (T. turgidum subsp. durum) landraces under water stressed and non stressed conditions

Ethiopia, being a major center of origin and diversity for durum wheat, possesses a highly variable genetic pool with diverse agroecological adaptations. Wheat landraces are an important source of genetic variation for breeding programs. This study was conducted to study the genotypic diversity of Ethiopian durum wheat genetic resources under two contrasting environments namely drought-stressed and non-stressed. It was carried out on 100 landraces and 4 local checks using an augmented design. Data were collected on 13 traits comprising yield and yield components, phenology, and canopy condition. The analysis of variance revealed significant differences between landraces for different traits with different sources of variation. Several landraces were found to outyield the checks at both environmental conditions. Intermediate to high estimates of the phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), heritability in a broad sense (h2b), and genetic advance in percent of the mean (GAPM) were observed for all the studied traits except for days to flowering at normal, thousands seed weight at stress, and days to maturity, leaf chlorophyll concentration measurement, and canopy temperature measurement at both conditions. The estimation of variability parameters showed that genotypic variation was higher than environmental variation for most traits. The number of tillers, spike length, kernel per spike, and grain yield indicated higher values for h2b and GAPM (74.42% and 20.86; 83.2% and 28.24; 70.79% and 28.0; and 89.54% and 74.71) at normal and (97.87% and 98.22; 71.27% and 28.51; 75.52% and 43.9; and 90.04% and 103.68) at the stressed condition, respectively. Spikelets per spike, kernel per spike, and thousands seed weight were positively correlated with grain yield. Grain yield exhibited a weak negative correlation with days to heading and days to maturity. Principal components analysis revealed that six traits were the major loadings on the first two principal components that describe 37.9% and 41.0% of the total morphological variance at normal and stressed conditions, respectively. Cluster analysis grouped the landraces into six clusters, with each cluster showing variation in performance for different traits under normal and stressed conditions. The intracluster distance was maximum in cluster I (D2 = 7.68) and (D2 = 8.19) at normal and stressed conditions respectively and the intercluster distance was found to be maximum between clusters I and IV (D2 = 11.02) and clusters I and II (D2 = 10.33) at normal and stressed conditions respectively. The presence of significant genetic variability among the evaluated durum wheat landraces suggests an opportunity for improvement of grain yield through the hybridization of genotypes from different clusters and subsequent selection. Genotypes with superior agronomic traits that outperform the best checks are identified as potential parents for yield improvement programs for moisture stress.

A rapid assay for assessing bacterial effects on Arabidopsis thermotolerance

BackgroundThe role of beneficial microbes in mitigating plant abiotic stress has received considerable attention. However, the lack of a reproducible and relatively high-throughput screen for microbial contributions to plant thermotolerance has greatly limited progress in this area, this slows the discovery of novel beneficial isolates and the processes by which they operate.ResultsWe designed a rapid phenotyping method to assess the effects of bacteria on plant host thermotolerance. After testing multiple growth conditions, a hydroponic system was selected and used to optimize an Arabidopsis heat shock regime and phenotypic evaluation. Arabidopsis seedlings germinated on a PTFE mesh disc were floated onto a 6-well plate containing liquid MS media, then subjected to heat shock at 45 °C for various duration. To characterize phenotype, plants were harvested after four days of recovery to measure chlorophyll content. The method was extended to include bacterial isolates and to quantify bacterial contributions to host plant thermotolerance. As an exemplar, the method was used to screen 25 strains of the plant growth promoting Variovorax spp. for enhanced plant thermotolerance. A follow-up study demonstrated the reproducibility of this assay and led to the discovery of a novel beneficial interaction.ConclusionsThis method enables rapid screening of individual bacterial strains for beneficial effects on host plant thermotolerance. The throughput and reproducibility of the system is ideal for testing many genetic variants of Arabidopsis and bacterial strains.

How do different functional crop groups perform in temperate silvoarable agroforestry systems? A Swiss case study

AbstractIntroductionAgroforestry systems provide a number of ecosystem services and are frequently considered as a promising diversification strategy for more sustainable and climate resilient primary production. However, most agroforestry field trials compare only one crop type with a control in open field. Additional comparisons between treatments that influence nutrient and water availability are often not looked at, nor are comparisons between crop species.Materials and MethodsTo deepen our understanding of the ecological processes underlying the potential benefits of agroforestry for food production, the present experimental study addresses three environmental factors (shade, fertilisation and irrigation) on three functionally different crop species (field bean, summer barley, summer rapeseed) and a C4‐grass (Echinochloa crus‐galli) in a Swiss agroforestry system. Crop performance (physiological traits, yield) between functional groups was analysed among treatment combinations of shade, fertilisation and irrigation. Physiological traits included measurements of chlorophyll content, stomatal conductance, specific leaf area and plant height.ResultsSummer barley and field bean showed significant yield declines when shaded (−44% and −38%, respectively), similar to summer rapeseed with a significant biomass decline (−35%). Shade significantly increased the occurrence of lodging in barley. Rapeseed in particular performed better when fertilised (+40% biomass).ConclusionThe results allow to estimate the range of potential yield losses in the competitive zone near mature trees for functionally different crop groups. The findings serve as a decision‐support for species selection in temperate European agroforestry systems.

Adaptive plasticity and fitness costs of endangered, nonendangered, and invasive plants in response to variation in nitrogen and phosphorus availabilities.

Global change drivers such as eutrophication and plant invasions will create novel environments for many plant species. Through adaptive trait plasticity plants may maintain their performance under these novel conditions and may outcompete those showing low-adaptive trait plasticity. In a greenhouse study, we determined if plasticity in traits is adaptive or maladaptive in endangered, nonendangered, and invasive plant species in response to variation of nitrogen (N) and phosphorus (P) availability (N:P ratios 1.7, 15, and 135) and whether plastic trait responses are adaptive and/or costly for fitness (i.e., biomass). Species choice comprised 17 species from three functional groups (legumes, nonlegume forbs, and grasses), either classified as endangered, nonendangered, or invasive. After 2 months, plants were harvested and nine traits related to carbon assimilation and nutrient uptake were measured (leaf area, SLA, LDMC, SPAD, RMR, root length, SRL, root surface area, and PME activity). We found more traits responding plastically to variation in P than in N. Plasticity only created costs when P was varied. Plasticity in traits was mostly adaptively neutral toward fitness, with plasticity in three traits being similarly adaptive across all species groups: SPAD (as a measure of chlorophyll content, adaptive to N and P limitation), leaf area, and root surface area (adaptive to P limitation). We found little differences in trait plasticity between endangered, nonendangered, and invasive species. Synthesis. Along a gradient from N limitation, balanced N:P supply, and P limitation, we found that the type of fluctuating nutrient (i.e., if N or P is varied) is decisive for the adaptive value of a trait. Variation in P availability (from balanced supply to P limitation) created both a stronger reduction in fitness as well as created plasticity costs in more traits than variation in N availability (from balanced supply to N limitation). However, the patterns observed in our study may change if nutrient availability is altered, either by nutrient inputs or by a shift in nutrient availabilities, for example, by decreasing N input as foreseen by European Legislation, but without simultaneously decreasing P input.

The Plant Growth-Promoting Bacteria Strain Bacillus mojavensis I4 Enhanced Salt Stress Tolerance in Durum Wheat.

Plant growth and production are adversely affected by soil salinity. A plant growth-promoting bacteria (PGPB) designated as the "I4 strain" of Bacillus mojavensis was isolated from Tunisian soil (Sfax, Tunisia) and showed the ability to be grown in the presence of NaCl concentrations ranging from 0 to 10% in Luria Bertani (LB) medium. The PGPB-mediated salt tolerance in durum wheat was evaluated. The physiological parameters such as growth, shoot and root length, dry and fresh weight were higher in I4-inoculated wheat plants in comparison with non-treated plants under salt stress. Results showed that this strain promoted wheat growth and preserved the membrane damage by notably lowering the electrolytes leakage and malondialdehyde content in contrast to non-inoculated plants. Moreover, leaf chlorophyll content, biochemical parameters and antioxidant enzyme activities measurement showed a better salt and heavy metal stress adaptation of the I4-inoculated plants. Due to these outcomes, it could be suggested that the inoculation of the PGPB I4 strain enhanced the wheat plant's growth, especially under salt stress conditions. This study confirms the ameliorative role played by PGPB in tolerating salt stress in wheat and their potential use as biofertilizers to enhance its growth in saline soil and help in promoting this plant's culture to provide food security under these perturbed global circumstances.