Leaf Chlorophyll Content Research Articles

Estimating crop leaf area index and chlorophyll content using a deep learning-based hyperspectral analysis method

The crop leaf area index (LAI) and leaf chlorophyll content (LCC) are essential indicators that reflect crop growth status, and their accurate estimation is helpful for agricultural management decision-making. Traditional hyperspectral estimation methods for crop LAI and LCC from canopy spectra face challenges due to intricate soil backgrounds, canopy structural environments, and varying observational conditions. This paper proposes an LAI and LCC estimation method based on hyperspectral remote sensing, a radiation transfer model (RTM), and a leaf area index and leaf chlorophyll content deep learning network (LACNet). The LACNet architecture was developed utilizing deep and shallow feature fusion, blocks, and a hyperspectral-to-image transform (HIT) concept, aiming to improve LAI and LCC estimation. We used a field-based spectrometer to collect a dataset comprising 1,234 spectral measurements across five crop types: wheat, maize, potato, rice, and soybean. We used properties optique spectrales des feuilles and scattering by arbitrarily inclined leaves (PROSAIL) to generate a simulated spectra dataset (n = 145,152) representing complex farmland conditions for the five abovementioned crops, considering the variations in soil type, soil moisture, LAI, LCC, etc. The LACNet deep learning model sequentially uses RTM simulated and field-based spectra datasets for training, achieving higher universality and validation accuracy. We also analyzed the LACNet model’s interpretability for LAI and LCC estimation based on the gradient-weighted class activation mapping theory. From our research, we drew the following conclusions: (1) The shallow network features are sensitive to the LAI and LCC in the entire visible band, consistent with our correlation analysis results, while the deep network sensitive areas are mainly concentrated in the RE + VIS and RE + NIR regions of the HIT images. (2) The LACNet deep learning model (LAI: coefficient of determination (R2) = 0.770, root mean square error (RMSE) = 0.968 m2/m2; LCC: R2 = 0.765, RMSE = 4.547 Dualex readings) can provide higher crop LAI and LCC estimation accuracy than widely used spectral feature and statistical regression methods (LCC: R2 = 0.491–0.620, RMSE = 5.804–6.691 Dualex readings; LAI: R2 = 0.476–0.716, RMSE = 1.089–1.482 m2/m2). The results of this study highlight the potential of the LACNet deep learning model as an effective and robust tool for accurately estimating crop LAI and LCC.

UAV multispectral remote sensing for the estimation of SPAD values at various growth stages of maize under different irrigation levels

Chlorophyll is crucial for photosynthesis in plants and the readings by a SPAD meter (Soil and Plant Analyzer Development) can be used to represent leaf chlorophyll content for monitoring crop growth status and predicting grain yield. Remote sensing technology has shown potential in non-destructive monitoring of SPAD values over large areas, but current SPAD inversion models are limited in their ability to incorporate multiple principal components besides spectral parameters, adapt to other variables such as water stress, and predict SPAD only throughout the entire growth period. This two-year study used crop parameters (plant height and leaf area index) and vegetation indices (VI) derived from unmanned aerial vehicle (UAV) multispectral images to develop SPAD prediction models for maize under different irrigation levels in the 2018 and 2019 growing seasons in Inner Mongolia, China. Two nonlinear machine learning models, random forest (RF) and support vector regression (SVR), and a multiple statistical regression method (partial least squares regression (PLSR)) were used to modeling SPAD. The results showed that the VIs with a high correlation with SPAD varied at each growth stage and the accuracy of SPAD estimation model can be improved significantly by dividing different growth stages (R2 increased by more than 104 %). PLSR performed better than RF and SVR for each growth period, especially at the reproductive R stage (R2 = 0.79, RMSE = 2.25). LAI and PH did not always improve prediction accuracy, but adding crop parameters did increase the correlation coefficient between predicted values and biomass by 8.3 %. This study provides valuable insights into the estimation of SPAD at different growth stages of maize under varying water stress levels using UAV data and crop parameters, offering guidance for farmland management and yield prediction.

A novel agrosinters support growth, photosynthetic efficiency and reduce trace metal elements accumulation in oilseed rape growing on metalliferous soil

Soil conditioners to fertilize, improve soil structure and support the phytostabilization of trace metal elements (TMEs) are being used more and more frequently. One of the options are agrosinters – slow-release ceramic fertilizers consisting mainly of SiO2, CaO, P2O5 and K2O, with an alkaline pH and high impact strength. The effect of two different agrosinters, A1 and A2, on the growth and physiological condition of Brassica napus grown in uncontaminated and Pb-, Cd- and Zn-contaminated soil was investigated in a pot experiment. In vivo data were collected using an infrared gas analyzer, a fluorimeter, a pigment content meter and a thermal camera. Elemental composition of the biomass was also investigated. The tested agrosinters promote biomass yield and have an effect on improving leaf chlorophyll content, phenomenological energy fluxes and plant gas exchange. The effect of the agrosinters on the plants was dose- and amendment-specific. An immobilization effect was observed not only in the soil but also in the plants. A reduction in the Cd (22%) and Zn (40%) content in the biomass was measured. All this was related to the effect of increasing the available form of P (50%), K (300%) and Ca (50%) in the soil, which improves soil fertility and reduces the bioavailable forms of the studied TMEs, due to the increase in soil pH and/or the complexation of these with phosphate compounds. The multidimensional analysis of A2 agrosinter shows the most positive effects on plant conditions, indicating that fly ash as a mixed substrate benefits the plants.

Effects of Melatonin on Ginseng Leaf Phenotypic and Photosynthetic Properties Following Chilling and Freezing Stress

Spring frost damage is a significant natural disaster, which can lead to large-scale ginseng (Panax ginseng C. A. Mey) yield reduction. To determine chilling and freezing stress damage to ginseng, we designed an experiment using three-year-old potted ginseng plants. We simulated chilling (0±0.3 ℃) and freezing (-2.5±0.3 ℃, -5±0.3 ℃) in an artificial room, with 16±2 ℃ of the room temperature at night as the control, to evaluate their physiological effects on ginseng leaf spectral reflectance, photosynthetic characteristics, and chlorophyll fluorescence parameters. We also investigated the mitigating effects of exogenous melatonin on ginseng leaves exposed to chilling and freezing stress. All ginseng leaves died under -5 ℃ freezing stress and the mortality rate reached 25∼57% under -2.5 ℃ freezing stress, while no plants died under 0 ℃ chilling stress. After -2.5 ℃ and 0 ℃ stress, leaf spectral reflectance in the 750∼1000 nm band was significantly lower than the control; transpiration rate, net photosynthetic rate, intercellular CO2 concentration, and stomatal conductance decreased significantly compared with the control, and there was no significant change in recovery time. The Fv/Fm ratio and qp decreased after low-temperature stress, while NPQ increased. After one week of melatonin application, leaf spectral reflectance in the 600∼650 and 750∼1000 nm bands reflected significant differences between the effects of melatonin application and no application on ginseng leaf chlorophyll content and structure under freezing stress. Ginseng leaf net photosynthetic rate increased by 23.79% and 4.48%, while intercellular CO2 concentration increased by 33.49 and 11.83% under stress at -2.5°C and 0°C. The ginseng leaf chlorophyll fluorescence parameter (Fv/Fm) recovered to 0.8 and above in one week after melatonin application, and qp decreased under -2.5°C and 0°C stress after melatonin application. Additionally, the NPQ values were 11.44% and 4.6% lower in -2.5+MT and 0+MT, respectively, compared to the treatment without MT applied. A sudden drop in temperature to 0 ℃ or even below caused ginseng plant growth inhibition, but melatonin application at a concentration of 100 nmol L−1 had certain mitigating effects on ginseng growth, which provides a theoretical basis and technical support for ginseng production.

Cyanobacterial Cultures, Cell Extracts, and Individual Toxins Decrease Photosynthesis in the Terrestrial Plants Lactuca sativa and Zea mays

Cyanobacterial harmful algal blooms (cHABs) are increasing due to eutrophication and climate change, as is irrigation of crops with freshwater contaminated with cHAB toxins. A few studies, mostly in aquatic protists and plants, have investigated the effects of cHAB toxins or cell extracts on various aspects of photosynthesis, with variable effects reported (negative to neutral to positive). We examined the effects of cyanobacterial live cultures and cell extracts (Microcystis aeruginosa or Anabaena flos-aquae) and individual cHAB toxins (anatoxin-a, ANA; beta-methyl-amino-L-alanine, BMAA; lipopolysaccharide, LPS; microcystin-LR, MC-LR) on photosynthesis in intact plants and leaf pieces in corn (Zea mays) and lettuce (Lactuca sativa). In intact plants grown in soil or hydroponically, overall net photosynthesis (Pn), but not Photosystem-II (PSII) electron-transport yield (ΦPSII), decreased when roots were exposed to cyanobacterial culture (whether with intact cells, cells removed, or cells lysed and removed) or individual toxins in solution (especially ANA, which also decreased rubisco activity); cyanobacterial culture also decreased leaf chlorophyll concentration. In contrast, ΦPSII decreased in leaf tissue vacuum-infiltrated with cyanobacterial culture or the individual toxins, LPS and MC-LR, though only in illuminated (vs. dark-adapted) leaves, and none of the toxins caused significant decreases in in vitro photosynthesis in thylakoids. Principal component analysis indicated unique overall effects of cyanobacterial culture and each toxin on photosynthesis. Hence, while cHAB toxins consistently impacted plant photosynthesis at ecologically relevant concentrations, the effects varied depending on the toxins and the mode of exposure.

Potential hazards of octinoxate (ethylhexyl methoxycinnamate) exposure in the seagrass Posidonia oceanica (L.) Delile: Experimental evidence

Interest has risen in recent years to assess the fate and impacts of organic ultraviolet (UV) filters in aquatic environments, after being recognized as contaminants of emerging concern. Octinoxate, also known as ethylhexyl methoxycinnamate (EHMC), is among the widely used UV filters in sunscreen formulations. Its potential toxicological effects in aquatic biota have led to precautionary actions in some jurisdictions, such as restricting and even banning its presence in commercial sunscreens. However, research data is lacking for effective regulation of sunscreen ingredients in Mediterranean countries. Our study evaluated the response of the seagrass Posidonia oceanica to EHMC under a short-term experimental setup, recreating summer conditions in aquaria subjected to different environmentally relevant concentrations for the Mediterranean Sea (minimum: 30.57 ng L−1, intermediate: 710 ng L−1 and maximum: 1420 ng L−1) and a control (no addition). Negative responses were evidenced in the physiology of P. oceanica due to EHMC exposure, with decreased gross primary production rates, loss of leaf chlorophyll content and inhibition of the overall nitrogen fixation associated with the seagrass tissues. Elevated oxidative stress biomarkers in P. oceanica under EHMC addition (i.e., catalase activity, polyphenols concentrations and alkaline phosphatase activity in leaves) reflect the activation of antioxidant defenses to counteract oxidative damages possibly induced in cellular components by the presence of the contaminant. Our results may conservatively imply the high ecological risk imposed to P. oceanica by maximum concentrations of EHMC in Mediterranean coastal waters. Moreover, they contribute as experimental evidence of the potentially detrimental effects of sunscreen pollution in a keystone species, such as P. oceanica, which is the main structural component of a priority habitat for conservation in the Mediterranean Sea, currently affected by multiple stress factors leading to its recession in recent years.

A multi-trait targeted genotype selection approach for screening drought tolerance in teosinte–maize hybrids

Drought tolerance is a complex characteristic and screening based on multiple traits rather than direct selection indices would result in more efficient selection. The study aims to select hybrids under optimal and water deficit conditions to achieve genetic gains in yield traits without compromising the other secondary traits viz., anthesis-silking interval, leaf chlorophyll content, delayed leaf rolling and leaf senescence, relative water content, and ROS scavenging using the multi-trait genotype ideotype distance index. The 30 teosinte-maize hybrids along with four checks were evaluated for two classes of traits: 16 morphometric and 14 physiological and biochemical traits, under well-watered (WW) and water-stress (WS) environments. Significant variations among the genotypes were observed for most traits and the presence of moderate to high heritability for most traits suggests direct selection for improvement of those traits. Significant correlation of the traits with yield and intercorrelations between traits indicate the advantage of indirect selection based on secondary traits. By assuming a selection intensity of 15% and equal weightage to all the traits, five genotypes were selected based on the MGIDI under each class of traits and in each environment. The genotypes viz. G19, G20, and G29 were commonly selected across both environments. The selection led to desired positive and negative selection gains for most of the traits studied and resulted in high positive gains for single plant yield of 35.6% under WW and 69.3% under WS. The strength and weakness plots effectively present the advantages and limitations of the selected genotypes under each environment. The multi-trait-based selection approach is an effective tool for selecting genotypes and designing breeding strategies for stress breeding.

A Leaf Chlorophyll Content Estimation Method for Populus deltoides (Populus deltoides Marshall) Using Ensembled Feature Selection Framework and Unmanned Aerial Vehicle Hyperspectral Data

Leaf chlorophyll content (LCC) is a key indicator in representing the photosynthetic capacity of Populus deltoides (Populus deltoides Marshall). Unmanned aerial vehicle (UAV) hyperspectral imagery provides an effective approach for LCC estimation, but the issue of band redundancy significantly impacts model accuracy and computational efficiency. Commonly used single feature selection algorithms not only fail to balance computational efficiency with optimal set search but also struggle to combine different regression algorithms under dynamic set conditions. This study proposes an ensemble feature selection framework to enhance LCC estimation accuracy using UAV hyperspectral data. Firstly, the embedded algorithm was improved by introducing the SHapley Additive exPlanations (SHAP) algorithm into the ranking system. A dynamic ranking strategy was then employed to remove bands in steps of 10, with LCC models developed at each step to identify the initial band subset based on estimation accuracy. Finally, the wrapper algorithm was applied using the initial band subset to search for the optimal band subset and develop the corresponding model. Three regression algorithms including gradient boosting regression trees (GBRT), support vector regression (SVR), and gaussian process regression (GPR) were combined with this framework for LCC estimation. The results indicated that the GBRT-Optimal model developed using 28 bands achieved the best performance with R2 of 0.848, RMSE of 1.454 μg/cm2 and MAE of 1.121 μg/cm2. Compared with a model performance that used all bands as inputs, this optimal model reduced the RMSE value by 24.37%. In addition to estimating biophysical and biochemical parameters, this method is also applicable to other hyperspectral imaging tasks.

Combining UAV Multispectral and Thermal Infrared Data for Maize Growth Parameter Estimation

The leaf area index (LAI) and leaf chlorophyll content (LCC) are key indicators of crop photosynthetic efficiency and nitrogen status. This study explores the integration of UAV-based multispectral (MS) and thermal infrared (TIR) data to improve the estimation of maize LAI and LCC across different growth stages, aiming to enhance nitrogen (N) management. In field trials from 2022 to 2023, UAVs captured canopy images of maize under varied water and nitrogen treatments, while the LAI and LCC were measured. Estimation models, including partial least squares regression (PLS), convolutional neural networks (CNNs), and random forest (RF), were developed using spectral, thermal, and textural data. The results showed that MS data (spectral and textural features) had strong correlations with the LAI and LCC, and CNN models yielded accurate estimates (LAI: R2 = 0.61–0.79, RMSE = 0.02–0.38; LCC: R2 = 0.63–0.78, RMSE = 2.24–0.39 μg/cm2). Thermal data reflected maize growth but had limitations in estimating the LAI and LCC. Combining MS and TIR data significantly improved the estimation accuracy, increasing R2 values for the LAI and LCC by up to 23.06% and 19.01%, respectively. Nitrogen dilution curves using estimated LAIs effectively diagnosed crop N status. Deficit irrigation reduced the N uptake, intensifying the N deficiency, while proper water and N management enhanced the LAI and LCC.

Smartphone-Based Leaf Colorimetric Analysis of Grapevine (Vitis vinifera L.) Genotypes

Leaf chlorophyll content is a key indicator of plant physiological status in viticulture; therefore, regular evaluation to obtain data for nutrient supply and canopy management is of vital importance. The measurement of pigmentation is most frequently carried out with hand-held instruments, destructive off-site spectrophotometry, or remote sensing. Moreover, smartphone-based applications also ensure a promising way to collect colorimetric information that could correlate with pigmentation. In this study, four grapevine genotypes were investigated using smartphone-based RGB (Red, Green, Blue) and CIE-L*a*b* colorimetry and a portable chlorophyll meter. The objective of this study was to evaluate the correlation between leaf chlorophyll concentration and RGB- or CIE-L*a*b*-based color indices. A further aim was to find an appropriate model for discriminating between the genotypes by leaf coloration. For these purposes, fully developed leaves of ‘Chardonnay’, ‘Sauvignon blanc’, and ‘Pinot noir’ clones 666 and 777 were investigated with the Color Grab smartphone application to obtain RGB and CIE-L*a*b* values. Using these color values, chroma, hue, and a further 31 color indices were calculated. Chlorophyll concentrations were determined using an Apogee MC100 device, and the values were correlated with color values and color indices. The results showed that the chlorophyll concentration and color indices significantly differed between the genotypes. According to the results, certain color indices show a different direction in their relationship with leaf pigmentation for different grapevine genotypes. The same index showed a positive correlation for the leaf chlorophyll concentration for one variety and a negative correlation for another, which raises the possibility that the relationship is genotype-specific and not uniform within species. In light of this result, further study of the species specificity of the commonly used vegetation indices is warranted. Support Vector Machine (SVM) analysis of the samples based on color properties showed a 71.63% classification accuracy, proving that coloration is an important ampelographic feature for the identification and assessment of true-to-typeness.

Enhanced remediation of acetochlor-contaminated soils using phosphate-modified biochar: Impacts on environmental fate, microbial communities, and plant health

Given that acetochlor (ACT) persists in soil for extended periods, disrupting microbial community structure and causing phytotoxicity to sensitive crops, this study investigated the potential of phosphate-modified biochar (PBC-800) to remediate ACT-contaminated soil. Incorporating 0.5 % PBC-800 into fluvo-aquic, red, and black soils increased their adsorption capacities by 80.4 mg g−1, 76.6 mg g−1, and 76.0 mg g−1, respectively. Even after six months of aging, the Kf values remained 1.6 to 5.1 times higher than in untreated soils. PBC-800 also accelerated ACT degradation across all three soil types, reducing residual ACT levels by 34.3 % to 76.4 % after 60 days, and shortening the degradation half-life by 5 to 7 days. High-throughput sequencing revealed that ACT reduced soil microbial diversity and disrupted community structure, while 0.5 % PBC-800 amendments promoted the growth of degradation-capable genera such as Rhodococcus, Lysobacter, and Gemmatimonas, enhancing microbial ecosystem stability. Furthermore, the amendment of soil with 0.5 % PBC-800 reduced ACT residue concentrations in maize and soybeans by 76.5 % to 82.9 %, and restored plant biomass, leaf chlorophyll content, and mesophyll cell ultrastructure to levels comparable to the control. Therefore, amending ACT-contaminated soil with PBC-800 mitigates ecological and environmental risks, boosts microbial activity, and safeguards plant health.

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.

Effect of Palm Oil Mill Effluent (Pome) Concentration and Soil Type on the Growth of Oil Palm (Elaeis guinensis Jacq) in Pre-Nursery

The place of research was carried out at Educational and Research Garden of the STIPER Agricultural Institute which is located in Maguwoharjo Village, Depok District, Sleman Regency, Yogyakarta, Indonesia. The research location is located 118 meters above sea level. This research was conducted between March to June 2024. Two components make up the factorial experiment used in the Complete Randomized Design (CRD). The soil type had three levels: A1 = latosol soil, A2 = regusol soil, and A3 = grumusol soil, was the first factor. Palm Oil Mill Effluent (POME) concentration was the second factor. It was consisted into four levels: X1 was the control (NPK fertilizer), X2 was 150 ml per polybag, X3 was 300 ml per polybag, and X4 was 450 ml per polybag. So there were 12 treatment combinations (3 x 4 = 12) with 4 repetitions, so all the experiment 48 plants. Analysis of variance (ANOVA) was applied at the significant level of 5% to check the observation data. Duncan's Multiple Range Test (DMRT) was then used at a noticeable 5% level if there was significant difference. The weight parameters of fresh roots and the amount of chlorophyll in the leaves are influenced by the interaction of the soil and POME concentration. The composition, regusol soil and POME 450 ml/polybag provide an optimal combination of treatments. The type of soil has an impact on the weight of fresh and dry crowns, the number of leaves, the chlorophyll content in the leaves, the volume of roots, and the height of the plant. The best type of soil is regusol soil. Plant height, root volume, fresh and dry crown weight, and leaf chlorophyll content are all affected by POME concentration. The concentration of POME 450 ml/polybag showed the best results.

Mixed application of raw amino acid powder and Trichoderma harzianum fertilizer for the prevention and management of apple replant disease

Apple replant disease (ARD) is mainly caused by biological factors, and it severely restricts the development of the apple industry. The use of biological control measures to alleviate ARD is critically important for the sustainable development of the apple industry. The effects of raw amino acid powder and Trichoderma harzianum fertilizer on plant biomass, leaf and root indexes, soil physical and chemical properties, soil enzyme activity, and the soil fungal community were studied under pot and field conditions using Malus hupehensis Rehd. seedlings and grafted trees (Fuji New 2001/M9T337) as experimental materials. We found that the application of the materials significantly promoted plant growth, increased the leaf photosynthesis and chlorophyll content, root respiration rate, root antioxidant enzyme activity, and soil enzyme activity, significantly decreased the number of Fusarium sp. in soil, and significantly increased the abundance of beneficial bacteria. In conclusion, the mixed application of raw amino acid powder and Trichoderma harzianum fertilizer can is an effective method for the prevention and management of ARD.

Artificial humic acid promotes carbon sequestration in rice-soil system

Artificial humic acid (AHA) is a new exogenous organic material with enormous carbon sequestration potential. However, the effects and mechanisms of applying AHA under different irrigation regimes on carbon sequestration in rice-soil systems still need to be clarified. This study applied carbon isotope labeling technology to analyze the photosynthetic carbon sequestration capacity, the carbon transport capacity of the root, and greenhouse gas emission flux in the rice-soil system under different irrigation regimes and AHA application conditions. The results showed that as the amount of AHA applied increased, the leaf area and chlorophyll concentration gradually increased, the Rubisco activity first increased and then decreased under two irrigation regimes. In alternate wetting and drying irrigation with 300 mg kg-1 AHA treatment (DWI3), the photosynthetic carbon fixation ability was the highest, with Pn, Fv/Fm, and qP increasing by 65.35%, 214.85%, and 43.15% compared to alternate wetting and drying irrigation without applying AHA treatment (DWI0), respectively. Meanwhile, during DWI3 treatment, the organic acid concentration in root exudates, root biomass, root length, root surface area, and root length density were also the highest. In summary, among all treatments in this study, the content of 13C in soil under DWI3 treatment was highest, closely related to the strongest photosynthetic carbon fixation and root transport capacity under this treatment, as well as the lower greenhouse gas emission flux. Therefore, it is recommended to apply 300 mg kg-1 AHA with alternate wetting and drying irrigation for carbon sequestration, water conservation, and sustainable agricultural development.

Unleashing the synergistic effect of promising fungicides: a breakthrough solution for combating powdery mildew in pea plants.

Pea powdery mildew, caused by Erysiphe pisi, is a major limitation to global pea production. The emergence of fungicide-resistant pathogen populations due to frequent and injudicious pesticide application highlights the importance of exploring the synergistic properties of fungicide combinations. This study investigated the efficacy of difenoconazole, thiophanate-methyl, and sulfur, both individually and in mixtures, against powdery mildew and assessed the interaction types between these fungicides. The results demonstrated that the combination of difenoconazole, thiophanate-methyl, and sulfur was the most effective in reducing, reducing disease severity to 6.10% and minimizing conidial production on foliage. Additionally, this fungicide combination reduced conidial germination by 89.26% in vitro and by 87.50% in a detached leaf assay compared to the control. The treatment also positively impacted leaf chlorophyll content (55.18), green pod yield (22.21 tons ha-1), seed yield (12.29 tons ha-1), and other yield-related parameters. Although statistically significant, this ternary fungicide combination was closely followed by the binary combination of thiophanate-methyl and sulfur, which was the only combination exhibiting synergism in both laboratory and field trials with a synergy factor (SF) > 1. In conclusion, this approach offers improved disease control as part of integrated disease management (IDM) while minimizing the risk of resistant pathogen strains.

Kitchen Waste Digestate and Digestate Biochar Fertilizer for Turfgrass Management and Nutrient Leaching

Slow-release fertilizer is generally used in turfgrass management to maintain quality turf without inducing excess vertical growth, to avoid frequent mowing. This study was designed to verify whether kitchen waste digestate and digestate biochar could serve as slow-release fertilizers to promote turf quality and reduce nutrient leaching risk in two turfgrass species over two years in controlled pot experiments. The results showed that the nutrient release of granular fertilizers made from kitchen waste digestate and digestate biochar was a two-phase process (an initial rapid phase, followed by a slow-release phase). The kitchen waste digestate showed higher nitrogen and phosphorus release ratios than the biochar and sheep manure. Compared to the reference sheep manure, fertilization with kitchen waste digestate and biochar increased the leaf chlorophyll content by 48.6% and 27.9%, and reduced senescent leaves by 15.6% and 11.1%, respectively, at 23 d after fertilization, leading to higher turf quality. Fertilization with kitchen waste digestate biochar significantly reduced the nutrient leaching risk compared to fertilization with kitchen waste digestate and sheep manure. Our results demonstrated the potential of reusing kitchen waste digestate and digestate biochar as slow-release fertilizers for promoting turf quality in turfgrass management.

Synergistic enhancement of biomass allocation from leaves to stem by far-red light and warm temperature can lead to growth reductions

The application of far-red (FR; 700–800 nm) light can improve plant growth, especially in leafy green vegetables, through enhancing leaf expansion and photon capture. However, higher levels of FR light may induce excessive stem elongation at the expense of leaf expansion. The morphological responses to FR light are mediated by phytochrome photoreceptors, with their activity further dependent on temperature. We aimed to quantify if different plant species respond differently to FR light and temperature conditions. Six economically-important plant species, including three cool-season species (lettuce, kale, and petunia) and three warm-season species (tomato, African marigold, and zinnia) were grown under three FR fractions [FR/(Red+FR); 0, 0.13, and 0.25] and two temperature set points (22 and 28 ℃) to characterize their growth and morphological responses. Increasing the FR fraction from 0 to 0.25 led to a 26–47 % increase in leaf expansion in lettuce, kale, petunia, and zinnia at 22 ℃. However, the total leaf area of tomato and African marigold decreased by 14–26 % as the FR fraction increased to 0.25 at 22 ℃. At a warmer temperature of 28 ℃, unlike the response observed under cooler temperature, increasing the FR fraction resulted in excessive stem elongation (a 36–101 % increase) and a 16–49 % reduction in leaf expansion in lettuce, kale, and petunia. For tomato, African marigold, and zinnia, the total leaf area increased by 15–26 % as the FR fraction increased from 0 to 0.13 at 28 ℃; however, further increasing the FR fraction from 0.13 to 0.25 resulted in a reduction in total leaf area. Across all six species, a high FR fraction combined with warm temperature synergistically stimulated stem elongation at the expense of leaf expansion. Shoot biomass responded to FR light and warm temperature similarly to leaf expansion in all six species. We further characterized the physiological responses to FR light and temperature in lettuce and tomato. In both crops, FR light generally increased the quantum yield of photosystem II, while decreasing the net CO2 assimilation rate per unit leaf area, chlorophyll and carotenoid contents, and chlorophyll a:b ratio. Additionally, FR light increased soluble sugar:starch ratio in leaves at 28 ℃, but not at 22 ℃, suggesting that the synergistic effect of FR light and warm temperature on stem elongation may be mediated by increased soluble sugar translocation from leaves to stem. We concluded that the enhanced stem growth under FR light and warm temperature can lead to reduced plant biomass. Our results further indicate that the interactive effects between FR light and temperature on plant growth and morphology were species-dependent, with distinct responses observed among species with different temperature preferences.

Selection of optimal spectral features for leaf chlorophyll content estimation

Leaf chlorophyll content (LCC) is crucial for monitoring the physiological processes of crops. Many studies have utilized spectral features to develop regression models for accurate LCC estimation, enabling the quantitative assessment and evaluation of crop growth status. The selection of optimal spectral features and regression algorithms significantly affects the precision of LCC estimation. In this study, we compared and analyzed the optimal spectral features for LCC estimation, as well as the consistency of machine learning methods across different crop types, phenology periods, and sensors. First, we extracted various spectral features, including the original spectral features (OS), first-order derivative spectral features (FDS), original continuum-removed spectra (CR) along with their four related derivative spectral features, principal component variables derived from different spectral features, and highly correlated spectral features with LCC. These extracted spectral features were then employed to construct the LCC models using six common regression algorithms on different datasets. Finally, we analyzed the optimal combination of spectral features and regression algorithms for accurate LCC estimation considering various dimensions, such as crop type, phenological period, and sensor used in observation conditions. The results demonstrate that the combinations of the principal component variables of continuum-removed derivative reflectance with the top 10 correlations with LCC (PCA_CRDR_R) combined with Gaussian process regression (GPR) can be considered as the optimal choice for estimating LCC under diverse observation conditions at a canopy scale, and its R2 can reach 0.62 for sugar beet LCC estimation; thus providing valuable theoretical guidance for selecting appropriate spectral features for LCC estimation.

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

ABSTRACT The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (C ab), maximum carboxylation rate (V cmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and C ab were reliably retrieved in most cases, whereas V cmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field.