Chlorophyll Index Research Articles

Multispectral UAV-Based Disease Identification Using Vegetation Indices for Maize Hybrids

In the future, the cultivation of maize will become more and more prominent. As the world’s demand for food and animal feeding increases, remote sensing technologies (RS technologies), especially unmanned aerial vehicles (UAVs), are developing more and more, and the usability of the cameras (Multispectral-MS) installed on them is increasing, especially for plant disease detection and severity observations. In the present research, two different maize hybrids, P9025 and sweet corn Dessert R78 (CS hybrid), were employed. Four different treatments were performed with three different doses (low, medium, and high dosage) of infection with corn smut fungus (Ustilago maydis [DC] Corda). The fields were monitored two times after the inoculation—20 DAI (days after inoculation) and 27 DAI. The orthomosaics were created in WebODM 2.5.2 software and the study included five vegetation indices (NDVI [Normalized Difference Vegetation Index], GNDVI [Green Normalized Difference Vegetation Index], NDRE [Normalized Difference Red Edge], LCI [Leaf Chlorophyll Index] and ENDVI [Enhanced Normalized Difference Vegetation Index]) with further analysis in QGIS. The gathered data were analyzed using R-based Jamovi 2.6.13 software with different statistical methods. In the case of the sweet maize hybrid, we obtained promising results, as follows: the NDVI values of CS 0 were significantly higher than the high-dosed infection CS 10.000 with a mean difference of 0.05422 *** and a p value of 4.43 × 10−5 value, suggesting differences in all of the levels of infection. Furthermore, we investigated the correlations of the vegetation indices (VI) for the Dessert R78, where NDVI and GNDVI showed high correlations. NDVI had a strong correlation with GNDVI (r = 0.83), a medium correlation with LCI (r = 0.56) and a weak correlation with NDRE (r = 0.419). There was also a strong correlation between LCI and GNDVI, with r = 0.836. NDRE and GNDVI indices had the correlation coefficients with a CCoeff. of r = 0.716. For hybrid separation analyses, useful results were obtained for NDVI and ENDVI as well.

Tetranychus ludeni (Acari: Tetranychidae) infestation triggers a spatiotemporal redox response dependent on soybean genotypes.

The redox homeostasis and photosynthetic pigments changes vary with Tetranychus ludeni infestation, with longer-cycle genotypes showing greater tolerance and efficiency in antioxidant defense. Infestations of Tetranychus ludeni Zacher (Tetranychidae) have been frequently observed in soybean plants. In this context, understanding the oscillation of redox homeostasis is crucial for detecting and assessing the stress levels caused in the plants by these organisms. The impacts of these infestations on redox metabolism and photosynthetic pigments are currently unknown. Therefore, we examined the hypothesis that T. ludeni infestations in soybean plants can influence redox homeostasis and photosynthetic pigments in a spatiotemporal manner, varying between different infestation times, modules and genotypes. For this purpose, soybean plants of the genotypes Monsoy, maturity group 5.7, and Brasmax, maturity group 6.3, grown in a controlled environment, were exposed to infestation and evaluated at two periods: 14 and 24days. A variation in the distribution of T. ludeni within the infested plants over time increased the activity of ascorbate peroxidase and catalase, especially in Monsoy, reducing the content of hydrogen peroxide and superoxide, which prevented lipid peroxidation in the apical region in both genotypes. In the basal region, low chlorophyll indices corroborated by the yellow coloration of trifoliate leaves, high levels of membrane stability loss, and accumulation of hydrogen peroxide and superoxide characterized senescent trifoliate leaves in Brasmax, 24days post infestation. Thus, the infestation of T. ludeni has a complex and significant impact on the redox metabolism of soybean plants, especially in shorter-cycle genotypes such as Brasmax. Furthermore, the oscillation of homeostasis can be considered as a good biochemical marker for selecting more suitable genotypes that are less sensitive and prone to infestations.

Nitrogen status of durum wheat derived from Sentinel-2 satellite data in Central Italy

In agriculture, nitrogen (N) is a key element in plant nutrition that affects, both positively and negatively, the productive and qualitative results of the crop. Accurate quantification of nitrogen levels is crucial for devising effective plant nutrition strategies. The objective of this study was to validate a novel method to estimate the N content at different phenological stages of durum wheat (Triticum durum Desf. cv. Iride) under different N management strategies (chemical synthetic fertilizer - SYN and organic fertilizer - ORG) in Italy, using the Nitrogen Nutrition Index (NNI)as a diagnostic tool for improving nitrogen fertilization timing and doses. The NNI is the ratio between the actual crop nitrogen content (Na) and the optimal level (Nc) required for ideal growth conditions.On ground level, Leaf Area Index (LAI) , Canopy reflectance, leaf chlorophyll content (LCC) and N concentration in leaves were measured. At landscape level, LAI and Canopy Chlorophyll Indices (CIs) were derived from Sentinel 2 (S2) multispectral images captured on the same days as the ground measurements: Chlorophyll Indexes were used for estimating the canopy chlorophyll content, CCC. Na in leaves and canopy were calculated from LCC and CCC respectively.In the study area, Nc is Nc=4.65LAI–0.35, R2=0.92. Among the tested Chlorophyll Indices (CIs) regression models, the linear regression was the more accurate to predict Na content, even though most of the tested Chlorophyll Indices (CIs) showed an R2 > 0.8,a. The best-performing spectral index in both calibration and validation steps resulted from the IRECI, with R2=0.90 and RMSE=0.31. The developed NNI well-captured the seasonal N dynamic for durum wheat, under different N management and meteorological conditions. The NNI calculated from S2 data for crop N status assessment, showed to be an accurate estimation of the Nitrogen Nutrition Index and can be used for the fertilization plans without costly on ground measurements.

Influence of biofertilizers on potato (Solanum tuberosum L.) growth and physiological modulations for water and fertilizers managing

Biofertilizers constitute a safe way to alleviate the impacts of water shortage and the overload of chemical fertilizers. This work focuses on the effects of Arbuscular Mycorrhizal Fungi (AMF) and Plant Growth-Promoting Rhizobacteria (PGPR) on potato's agro-physiological response, beneath the reduction of water, nitrogen (N), phosphorous (P) and potassium (K) fertilizers. Five microbioal treatments (C: Control, AMF: Mycorhizae, B1: Bacillus halotolerans, B2: Bacillus amyloliquefaciens and B1B2: B.halotolerans+ B.amyloliquefaciens) were tested in arrangement with two nitrogen-phosphorous-potassium (NPK) rates (F1: Entire rate, F ½: Half rate) and three irrigation levels (100 % of crop water requirement: 100 % ETc; 75 % ETc; 50 % ETc) in a two year factorial split plot design. We studied vegetative growth, photosynthetic activity, chlorophyll fluorescence, water status, osmoticums, N, P, K and enzymatic antioxidants accumulation. Data obtained in 2019–2022 showed that AMF and B1B2 biofertilizers were beneficial to improve Root/Shoot, Dry Underground Part/Aerial Part, leaf area (LA), water saturation deficit (WSD), chlorophyll index (SPAD) and tuber yield under 75 % ETc. The photosynthetic promoter effect of biofertilizers under 75 % ETc, compared to 50 % ETc was observed in the following trend B1B2>AMF>B1. The efficacy of B1B2 and AMF were coupled to high catalase (CAT) activity. Potatoes treated with B1B2 were found to be efficient for 75 % ETc in terms of proline accumulation. The AMF was more effective in terms of soluble sugars (SS) accumulation and control of osmotic potential (Ψs). Taking together, the B1B2 and AMF could be regarded as efficient biofertilizers under 75 % ETc, alleviating the impacts of water stress and the problem of excessive fertilization in potatoes.

A novel recursive sub-tensor hyperspectral compressive sensing of plant leaves based on multiple arbitrary-shape regions of interest

Plant hyperspectral images (HSIs) contain valuable information for agricultural disaster prediction, biomass estimation, and other applications. However, they also include a lot of irrelevant background information, which wastes storage resources. In this paper, we propose a novel recursive sub-tensor hyperspectral compressive sensing method for plant leaves. This method uses recursive sub-tensor compressive sensing to compress and reconstruct each arbitrary-shape leaf region, discarding a large amount of background information to achieve the best possible reconstruction performance of the leaf region and significantly reduce storage space. The proposed method involves several key steps. Firstly, the optimal band is determined using the spatial spectral decorrelation criterion, and its corresponding mask image is used to extract the leaf regions from the background. Secondly, the recursive maximum inscribed rectangle algorithm is applied to obtain rectangular sub-tensors of leaves recursively. Each sub-tensor is then individually compressed and reconstructed. Finally, all sub-tensors can be reconstructed to form complete leaf HSIs without background information. Experimental results demonstrate that the proposed method achieves superior image reconstruction quality at extremely low sampling rates compared to other methods. The proposed method can improve average Peak Signal-to-Noise Ratio (PSNR) values by about 3.04% and 0.74% compared to Tensor Compressive Sensing (TCS) at the sampling rate of 2%. In the spectral domain, the proposed method can achieve significantly smaller Spectral Angle Mapper (SAM) values and relatively lower spectral indices errors for Double Difference, Triangular Vegetation Index, Leaf Chlorophyll Index, and Modified Normalized Difference 680 than those of TCS. Therefore, the proposed method achieves better compression performance for reconstructed plant leaf HSIs than the other methods.

A Novel Approach for Ex Situ Water Quality Monitoring Using the Google Earth Engine and Spectral Indices in Chilika Lake, Odisha, India

Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities and natural processes. Traditional in situ water quality monitoring methods are often labor intensive and time consuming. This study presents a novel approach for ex situ water quality monitoring in Chilika Lake, located on the east coast of India, utilizing Google Earth Engine (GEE) and spectral indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and total suspended solids (TSS). The methodology involves the integration of multi-temporal satellite imagery and advanced spectral indices to assess key water quality parameters, such as turbidity, chlorophyll-a concentration, and suspended sediments. The NDTI value in Chilika Lake increased from 2019 to 2021, and the Automatic Water Extraction Index (AWEI) method estimated the TSS concentration. The results demonstrate the effectiveness of this approach in providing accurate and comprehensive water quality assessments, which are crucial for the sustainable management of Chilika Lake. Maps and visualization are presented using GIS software. This study can effectively detect floating algal blooms, identify pollution sources, and determine environmental changes over time. Developing intuitive dashboards and visualization tools can help stakeholders engage with data-driven insights, increase community participation in conservation, and identify pollution sources.

Genome-wide association study reveals effect of nsSNPs on candidate genes in rice during iron deficiency.

Resource-poor areas with moisture deficit lands following aerobic and direct seeded rice (DSR) methods of cultivation face severe problems of iron deficiency. In this study, Bengal and Assam Aus rice panel was phenotyped at the seedling stage using an iron-deprived hydroponic medium for various shoot and root traits. A novel iron deficiency scoring scale was used to classify the tolerance reaction and could range anywhere between 0 and 9, indicating the most tolerant and susceptible, respectively. The GWAS results identified four putative candidate genes; OsFLA for number of leaves and shoot length, OsBIDK1 for root traits; average diameter, volume, biomass, projected area, and surface area, OsHPL3 for chlorophyll index of the third leaf and AKR2B (XBOS252) was for Fe score, (which was earlier reported in relation to Xa21). The nsSNP (nsSNPs) variations in these gene sequences were used to group the panel and identify superior haplotypes and donors. BR16 was identified as a superior donor, with higher chlorophyll index and shoot length than RA23, also higher values for root traits like root average diameter, root volume, root projected area and root surface area followed by Shete Bhado. The impact of identified nsSNPs on protein structure and stability was investigated. The conserved domains detected in the mutated proteins of the superior haplotypes are very informative, highlighting that natural selection favors abiotic stress tolerant variants in resource poor areas. Thus, justifying our choice of Aus landraces for association mapping of Fe deficiency tolerant genes in rice.

Deriving Vegetation Indices for 3D Canopy Chlorophyll Content Mapping Using Radiative Transfer Modelling

Leaf chlorophyll content is a major indicator of plant health and productivity. Optical remote sensing estimation of chlorophyll limits its retrievals to two-dimensional (2D) estimates, not allowing examination of its distribution within the canopy, although it exhibits large variation across the vertical profile. Multispectral and hyperspectral Terrestrial Laser Scanning (TLS) instruments can produce three-dimensional (3D) chlorophyll estimates but are not widely available. Thus, in this study, 14 chlorophyll vegetation indices were developed using six wavelengths employed in commercial TLS instruments (532 nm, 670 nm, 808 nm, 785 nm, 1064 nm, and 1550 nm). For this, 200 simulations were carried out using the novel bidirectional mode in the Discrete Anisotropic Radiative Transfer (DART) model and a realistic forest stand. The results showed that the Green Normalized Difference Vegetation Index (GNDVI) of the 532 nm and either the 808 nm or the 785 nm wavelengths were highly correlated to the chlorophyll content (R2 = 0.74). The Chlorophyll Index (CI) and Green Simple Ratio (GSR) of the same wavelengths also displayed good correlation (R2 = 0.73). This study was a step towards canopy 3D chlorophyll retrieval using commercial TLS instruments, but methods to couple the data from the different instruments still need to be developed.

Growth and initiation of third stage of immature oil palm (Elaeis guineensis Jacq.) by giving oil palm frond compost and paclobutrazol

The third stage of immature oil palm is the phase where the plants enter preparation for flowering, so efforts are needed to initiate flowering, one of which is by using plant growth regulators (PGR) such as paclobutrazol. It is expected that the use of oil palm fronds as compost and application to oil palm plants can support sustainable oil palm management activities. Paclobutrazol, as a growth regulator, plays a role in stimulating the flowering of immature oil palm plants. This research aims to determine the response to the growth and development of immature oil palm plants in the third stage by administering palm frond compost and paclobutrazol at certain doses and concentrations. The experiment was carried out from June to December 2020 at the Ciparanje Experimental Garden, Faculty of Agriculture, Padjadjaran University. The experimental design used was a Randomized Block Design (RBD) with 12 treatments and three replications. The experimental results showed that the application of compost to oil palm fronds and paclobutrazol had a significant effect on plant height and stem girth, but there were no significant differences in the number of fronds, leaf area, chlorophyll index, and flowers. All treatments did not produce flower shoots but showed inhibition of vegetative growth in immature oil palm plants in the third stage. The application of 12.8 kg of oil palm frond compost and 150 ppm paclobutrazol had the best effect on the height and girth of the oil palm stem and had an effect that was not significantly different from other treatments on the parameters of a number of fronds, leaf area, leaf chlorophyll, and flower growth.

Early-life exposure to residential greenness and risk of asthma in a U.S. bronchiolitis cohort.

Severe bronchiolitis (i.e., bronchiolitis requiring hospitalization) is linked to childhood asthma development. Despite a growing understanding of risk factors for developing post-bronchiolitis asthma, protective factors remain unclear. In this study, we aimed to investigate whether exposure to residential greenness between birth and bronchiolitis hospitalization is associated with asthma and atopic asthma development by age 6 years. We analyzed a US severe bronchiolitis cohort from hospitalization to age 6 years, investigating how the normalized difference vegetation index (NDVI) and chlorophyll index green (CI green), measured in small (100 m) and large (500 m) radiuses around homes, relate to asthma and atopic asthma by age 6 years. We also explored whether maternal antibiotic use, daycare attendance, and respiratory virus type during hospitalization act as effect modifiers. The study cohort included 861 infants, with 239 (28%) developing asthma by age 6 years-152 atopic, 17 nonatopic, and 70 unclassified. Early life residential exposure to high NDVI and CI green levels was associated with lower odds of asthma (ORAdj for NDVI within a 100 m radius, 0.18; 95% CI, 0.05-0.78; and ORAdj for CI green levels within a 100 m radius, 0.53; 95% CI, 0.31-0.90). Associations also were significant for the development of atopic asthma (ORAdj 0.16; 95% CI, 0.03-0.96; and ORAdj 0.46; 95% CI, 0.25-0.92; respectively). Results were similar for the 500 m radius exposures. No effect modification was noted. In a U.S. bronchiolitis cohort, exposure to residential greenness between birth and bronchiolitis hospitalization is linked to lower asthma and atopic asthma risk by age 6 years.

Non-Invasive Detection of Nitrogen Deficiency in Cannabis sativa Using Hand-Held Raman Spectroscopy

Proper crop management requires rapid detection methods for abiotic and biotic stresses to ensure plant health and yield. Hemp (Cannabis sativa L.) is an emerging economically and environmentally sustainable crop capable of yielding high biomass. Nitrogen deficiency significantly reduces hemp plant growth, affecting photosynthetic capacity and ultimately decreasing yield. When symptoms of nitrogen deficiency are visible to humans, there is often already lost yield. A real-time, non-destructive detection method, such as Raman spectroscopy, is therefore critical to identify nitrogen deficiency in living hemp plant tissue for fast, precise crop remediation. A two-part experiment was conducted to investigate portable Raman spectroscopy as a viable hemp nitrogen deficiency detection method and to compare the technique’s predictive ability against a handheld SPAD (chlorophyll index) meter. Raman spectra and SPAD readings were used to train separate nitrogen deficiency discrimination models. Raman scans displayed characteristic spectral markers indicative of nitrogen deficiency corresponding to vibrational modes of carotenoids, essential pigments for photosynthesis. The Raman-based model consistently predicted nitrogen deficiency in hemp prior to the onset of visible stress symptoms across both experiments, while SPAD only differentiated nitrogen deficiency in the second experiment when the stress was more pronounced. Our findings add to the repertoire of plant stresses that hand-held Raman spectroscopy can detect by demonstrating the ability to provide assessments of nitrogen deficiency. This method can be implemented at the point of cultivation, allowing for timely interventions and efficient resource use.

Prediction of urban surface water quality scenarios using hybrid stacking ensembles machine learning model in Howrah Municipal Corporation, West Bengal

In the pursuit of understanding surface water quality for sustainable urban management, we created a machine learning modeling framework that utilized Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splines (MARS), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and their hybrid stacking ensemble RF (SE-RF), as well as stacking Cubist (SE-Cubist), to predict the distribution of water quality in the Howrah Municipal Corporation (HMC) area in West Bengal, India. Additionally, we employed the ReliefF and Shapley Additive exPlanations (SHAP) methods to elucidate the underlying factors driving water quality. We first estimated the water quality index (WQI) to model seven water quality parameters: total hardness (TH), pH, total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD), calcium (Ca), magnesium (Mg). Then six independent factors were utilized (i.e. Precipitation (Pr), Maximum Temperature (Tmax), Minimum Temperature (Tmin), Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and Total Dissolved Solids (TDS)) for predicting the WQI mapping through the different ML models. This study demonstrated that the SE-Cubist model outperforms other ML models. During the testing phase, it achieved the best modeling results with an R2 = 0.975, RMSE = 0.351, and MAE = 0.197. The ReliefF and SHAP analyses identified Pr and Tmax as the most significant factors influencing WQI within the study area.

Developing a New Method to Rapidly Map Eucalyptus Distribution in Subtropical Regions Using Sentinel-2 Imagery

Eucalyptus plantations with fast growth and short rotation play an important role in improving economic conditions for local farmers and governments. It is necessary to map and update eucalyptus distribution in a timely manner, but to date, there is a lack of suitable approaches for quickly mapping its spatial distribution in a large area. This research aims to develop a uniform procedure to map eucalyptus distribution at a regional scale using the Sentinel-2 imagery on the Google Earth Engine (GEE) platform. Different seasonal Senstinel-2 images were first examined, and key vegetation indices from the selected seasonal images were identified using random forest and Pearson correlation analysis. The selected key vegetation indices were then normalized and summed to produce new indices for mapping eucalyptus distribution based on the calculated best cutoff values using the ROC (Receiver Operating Characteristic) curve. The uniform procedure was tested in both experimental and test sites and then applied to the entire Fujian Province. The results indicated that the best season to distinguish eucalyptus forests from other forest types was winter. The composite indices for eucalyptus–coniferous forest separation (CIEC) and for eucalyptus–broadleaf forest separation (CIEB), which were synthesized from the enhanced vegetation index (EVI), plant senescing reflectance index (PSRI), shortwave infrared water stress index (SIWSI), and MERIS terrestrial chlorophyll index (MTCI), can effectively differentiate eucalyptus from other forest types. The proposed procedure with the best cutoff values (0.58 for CIEC and 1.29 for CIEB) achieved accuracies of above 90% in all study sites. The eucalyptus classification accuracies in Fujian Province, with a producer’s accuracy of 91%, user’s accuracy of 97%, and overall accuracy of 94%, demonstrate the strong robustness and transferability of this proposed procedure. This research provided a new insight into quickly mapping eucalyptus distribution in subtropical regions. However, more research is still needed to explore the robustness and transferability of this proposed method in tropical regions or in other subtropical regions with different environmental conditions.

Constructing 3D SPAD distribution using hyperspectral LiDAR point cloud by PROSPECT model inversion

ABSTRACT Accurately estimating leaf chlorophyll concentrations in a three-dimensional (3D) manner is crucial for precisely monitoring crop growth and development. To reduce destructive detection and achieve real-time estimation, researchers often utilize crop leaves’ SPAD (Soil and Plant Analyzer Development) to represent relative chlorophyll content. However, SPAD measurement is time-consuming and laborious work. Many inversion methods have been proposed to estimate SPAD based on one-dimensional reflectance or two-dimensional spectral images, which rely on external light sources and also cannot obtain 3D distribution. Hyperspectral LiDAR (HSL), as an active remote sensing technology, can acquire target spatial and spectral data simultaneously, making 3D SPAD inversion and reconstruction possible. This research aims to invert SPAD values with HSL spectral information aided by the PROSPECT model and reconstruct 3D SPAD distribution with HSL spatial information. Firstly, we constructed new ratio chlorophyll indices (RCI) based on the multi-feature analysis method with the PROSPECT model and HSL spectral data. Next, we employed RCIs to modify the Look-Up Table (LUT) cost function (SPAD-LUT) of the PROSPECT model, which was validated with the ANGERS dataset. Then, we conducted SPAD inversion with HSL spectral information from vegetable crop samples and integrated the inverted SPAD values into their corresponding 3D coordinates, achieving 3D SPAD distribution reconstruction. The predicted values of the model inversion with the HSL dataset are consistent with the measured SPAD (R2 = 0.421, RMSE = 10.615). The results indicate that our method can invert sample SPAD values and reconstruct their distribution with HSL point cloud despite their complex spatial structure.

Responses of 'Flordaguard' and 'MP-29' Prunus spp. rootstocks to hypoxia and high root zone temperature.

Selecting the right rootstock is crucial for successful fruit production and managing both biotic and abiotic stresses in commercial fruit orchards. To enhance the resilience of peach orchards, this study evaluated the physiological and biochemical responses of Prunus spp. rootstocks , 'Flordaguard' and 'MP-29,' under normoxia (sufficient oxygen content) or short-term hypoxia (low-oxygen content) and ambient or high temperature (40°C) in the root zone. Physiological responses measured were net photosynthesis, stomatal conductance, transpiration, intercellular CO2 concentration, water use efficiency, the leaf chlorophyll index, and the maximum potential quantum efficiency of photosystem II. The leaf concentrations of nitrogen, phosphorus, potassium, magnesium, calcium, sulfur, boron, zinc, manganese, iron, and copper were also analyzed. Reactive oxygen species (ROS) and antioxidants analyzed were superoxide dismutase (SOD) activity, peroxidase (POD) activity, catalase (CAT) activity, ascorbate peroxidase (APX) activity, glutathione peroxidase (GPX) activity, proline content, glycine betaine content (GB), lipid peroxidation (LPO), superoxide (O2 -) concentration, and hydrogen peroxide (H2O2) concentration. When subjected to root zone hypoxia or high temperature individually, 'MP-29' performed better physiologically than 'Flordaguard'. However, when root zone hypoxia and high temperature were combined, 'MP-29' performed better biochemically with enhanced antioxidant activity, osmolyte content, and nutrient absorption. Nutrient analysis of leaves revealed that 'MP-29' had higher N, P, K, Ca, and B concentrations than 'Flordaguard'. Consequently, 'MP-29' demonstrated greater tolerance to short-term exposure to the combined effects of high root zone temperature and hypoxia. This research contributes to identifying a suitable rootstock within the Prunus genus able to withstand root zone conditions that often result from severe weather events commonly experienced in Florida and other parts of the world.

Evaluation of the growth, enzymatic activity, electrolyte leakage, and phytoremediation efficiency of Conocarpus erectus under cadmium and lead stress.

Contamination of agricultural soil by heavy metals poses a significant threat to soil quality and crop yields. Using plants as a natural remediation approach attracts researchers' attention around the world. A 16-month pot experiment was conducted using Conocarpus erectus in a randomized complete block design. The growth, enzymatic activity, electrolyte leakage, and remediation potential were estimated under Cd nitrate]40 low (L), 60 medium (M), 80 high (H) mg/kg soil [and Pb nitrate]400 (L), 700 (M), 1,000 (H) mg/kg soil [applied individually and in combination. Conocarpus erectus demonstrated a good tolerance (over 70%) against lower and medium cadmium (Cd) and lead (Pb) levels and a medium resistance against high Cd and Pb levels, with a survival rate of 100% under all the treatments used. The most negative treatment on the growth traits and tolerance of C. erectus was (H) Cd and (H) Pb, which reduced plant height; chlorophyll index; dry weights of the leaves, stems, and roots; root length; and tolerance index of biomass and roots by 25.87%, 48.97%, 50.56%, 47.25%, 58.67%, 50.18%, 51.00%, and 50% in comparison to the respective control, consecutively. Relative to the control, all Cd and Pb applications increased polyphenol oxidase (PPO), peroxidase (POD), and catalase (CAT) activities, and the increment was parallel up to medium Cd and Pb levels and then decreased with their high levels but still higher than the control. Electrolyte leakage (EL) was upheaved by raising the levels of Cd and Pb, and it reached the maximum (52.79%) at the (H) Cd (H) Pb treatment. Cd and Pb in the leaves, stems, and roots were boosted by raising their levels in the treatments. Conocarpus erectus is considered a phytoextractor for the Cd levels used because the bioconcentration factor of the stem (BCFs) and the translocation factor (TF) of Cd were >1, and it is a suitable plant for Pb phytoextraction at (L) Pb, (M) Pb, and (M) Cd (M) Pb levels because its Pb BCFs and bioconcentration factor of the root (BCFr) were <1 and its Pb TF was >1. On the other hand, C. erectus is considered a phytostabilizator for Pb at (H) Pb, (L) Cd, (L) Pb, and (H) Cd (H) Pb levels because its Pb BCFs, BCFr, and TF were <1.

Optimal fertilization of Marandu grass with gelatin industry sludge

This study determined optimal fertilization levels for Marandu grass (Urochloa brizantha cv. Marandu) as a forage using sludge from the gelatin industry. The experiment was performed in a greenhouse with five treatments: three rates of gelatin sludge application (0, 200, and 300 m3 ha-1), an organo-mineral (100 m3 ha-1 + mineral: 60 mg dm-3 N), and pure mineral fertilizer (100 mg dm-3 N) with six replicates. Plant and stem height, number of tillers, leaves, leaves/tiller, chlorophyll index, dry mass (leaves, roots, and residue), crude protein, neutral and acid detergent fiber, macronutrient, and micronutrient concentrations were measured. Plant and stem height, number of tillers, leaves, and biomass production increased linearly as gelatin sludge dosage increased. There was a linear increase in crude protein with reduction in neutral detergent fiber and acid detergent fiber with increasing gelatin sludge fertilization. Gelatin sludge dosage did not affect the contents of P, Mg, S, Cu, and Fe. Only Mn had a positive linear response to gelatin sludge with reduction at the highest rate. The 300 m3 ha-1 dosage of gelatin sludge increased biomass production and chlorophyll index. In contrast, the organo-mineral fertilizer promoted higher crude protein and lower neutral detergent fiber concentrations in grass. The gelatin sludge dosage of 300 m3 ha-1 improved the nutritive value of Marandu grass compared to mineral fertilizer. Gelatin sludge is a promising pasture amendment for growing Marandu grass as it recycles industrial animal waste for a more integrated livestock-cropping system.

Jasmonate and salicylic sprays improve cherry tomato fruit productivity and quality in unheated greenhouses

Introduction. Arable regions often have insufficient soil nutrients, which means that fertilizers must be used. However, using fertilizers excessively can harm the environment, human health, and food safety. Natural growth regulators derived from plants are environmentally friendly and reasonably priced. Salicylic acid (SA) and methyl jasmonate (MeJA) phytohormones are crucial for enhancing plant biomass, quality, productivity, and resistance to environmental stresses. Objective. To evaluate the foliar effect of MeJA and SA on fruit quality and production of cvs. colorful tomato under greenhouse conditions. Materials and methods. The trial was carried out in the University of Diyala, Iraq, from 1 December, 2022 to 5 June 2023. Foliar of 0, 200 mg/L SA and or 200 mg/L MeJA was evaluated to determine effects on fruit quality and production of colorful cherry tomato cultivars indeterminate: LA4013, LA353, LA2921, LA3899 and IQ2, with a randomized complete block design with each treatment having three replicates. Results. The cv. IQ2 had the heaviest fruit (82.13 g), highest yield (4.56 kg/plant), total yield (4.56 t/house) and highest treatable acidity (0.62 %). The cv. LA4013 had a higher fruit number (612.88 fruit/plant) and the highest total soluble solids (2.933 %). Foliar of SA at 200 mg/L had the highest chlorophyll index (36.68 SPAD) highest fruits per plant (373.73), highest yield (3.15 kg/plant) and total yield (3.93 t/house). Conclusion. When considering all variables together, the best genotypes were IQ2 and LA4013, which were found to be superior to the other genotypes in quality and yield trials. The 200 mg/L foliar SA improved the yield and quality of cherry tomatoes under greenhouse conditions.

Pengaruh Bulai pada Perubahan Indeks Kadar Klorofil, Serapan Fosforus dan Boron pada Jagung Manis

Maize production and quality are affected by infection with plant pathogens. One of the maize's essential and main diseases is downy mildew caused by Peronosclerospora spp. Downy mildew is a limiting factor in increasing production and can reduce production by 80-100%. It is because the affected plant cannot produce cobs. Pathogens obtain nutrients from the host cell, which can kill the cell and damage the surrounding tissues, resulting in visible downy mildew symptoms. Boron (B) plays a role in forming phloem, increasing the fruit's number, weight, bunch weight, and diameter. The primary function of B at the molecular level is the cross-linking of pectin in the plant cell wall. Ramnogalacturonan II (RG II) is a pectic polysaccharide that contributes to the mechanical strength and properties of the primary wall cross-linked by borate diesters. Phosphorus (P) controls the downsides in the greenhouse and field conditions. This study aims to measure changes in chlorophyll index, P and B uptakes in downy mildew affected plants. The field experiment used a group randomized design with six natural phosphate (FA) application treatments and four groups of borax doses as replicates. The results showed that the downy mildew decreased the chlorophyll index of the leaves at different levels of attack. The results of P concentration according to the position of healthy plant leaves were significantly different due to P treatment. In contrast to concentration B, there is no real difference. P and B uptake results in downy mildew-infested plants showed a significant difference only in P uptake in leaves with 1 FA treatment. Keywords: boron, downy mildew, maize, phosphate

Impact of High-density Planting Spacing on Physiological Traits of Cocoa Grown under Coconut Trees

High-density planting (HDP) maximizes land productivity. Optimizing cocoa spacing under coconut trees enhances physiological traits and yield potential. Despite cocoa's integration into coconut agroforestry, spacing's impact on cocoa physiology is unclear. Studying this influence provides insights for better planting strategies, improved crop performance, and sustainable cocoa production in agroforestry settings. The experiment was conducted at the Coconut Farm of the Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore. Employing a Randomized Block Design (RBD) with eight treatments replicated three times, the study aimed to explore how different spacing levels influence physiological traits in cocoa cultivation. The treatments involved in the experiment included a double row of cocoa planted between two rows of coconut trees, with spacing configurations as follows: T1 (3m x 1.2m), T2 (3m x 2m), T3 (3m x 2.5m), and T4 (3m x 3m). Additionally, a single row of cocoa between two coconut rows was examined, with spacings represented by T5 (1.5m), T6 (2m), T7 (2.5m), and T8 (3m). Results indicated distinct patterns among spacing treatments, with significant differences observed in various physiological characteristics. Notably, T1 (3m x 1.2m) demonstrated the highest leaf area (462.71cm2) and leaf area index (4.85), while T8 (3m) exhibited the highest light interception (74.12%). Additionally, T3 (3m x 2.5m) showcased the highest chlorophyll index (40.52) in cocoa leaves. These findings underscore the importance of spacing configurations in influencing key physiological parameters in cocoa cultivation, providing valuable insights for optimizing planting practices.