Maize Chlorophyll Research Articles

Unveiling the efficacy of Bacillus faecalis and composted biochar in alleviating arsenic toxicity in maize

Arsenic (As) contamination is a major environmental pollutant that adversely affects plant physiological processes and can hinder nutrients and water availability. Such conditions ultimately resulted in stunted growth, low yield, and poor plant health. Using rhizobacteria and composted biochar (ECB) can effectively overcome this problem. Rhizobacteria have the potential to enhance plant growth by promoting nutrient uptake, producing growth hormones, and suppressing diseases. Composted biochar can enhance plant growth by improving aeration, water retention, and nutrient cycling. Its porous structure supports beneficial microorganisms, increasing nutrient uptake and resilience to stressors, ultimately boosting yields while sequestering carbon. Therefore, the current study was conducted to investigate the combined effect of previously isolated Bacillus faecalis (B. faecalis) and ECB as amendments on maize cultivated under different As levels (0, 300, 600 mg As/kg soil). Four treatments (control, 0.5% composted biochar (0.5ECB), B. faecalis, and 0.5ECB + B. faecalis) were applied in four replications following a completely randomized design. Results showed that the 0.5ECB + B. faecalis treatment led to a significant rise in maize plant height (~ 99%), shoot length (~ 55%), root length (~ 82%), shoot fresh (~ 87%), and shoot dry weight (~ 96%), root fresh (~ 97%), and dry weight (~ 91%) over the control under 600As stress. There was a notable increase in maize chlorophyll a (~ 99%), chlorophyll b (~ 81%), total chlorophyll (~ 94%), and shoot N, P, and K concentration compared to control under As stress, also showing the potential of 0.5ECB + B. faecalis treatment. Consequently, the findings suggest that applying 0.5ECB + B. faecalis is a strategy for alleviating As stress in maize plants.

Enhancing maize resilience to drought stress: the synergistic impact of deashed biochar and carboxymethyl cellulose amendment

Drought stress poses a significant challenge to maize production, leading to substantial harm to crop growth and yield due to the induction of oxidative stress. Deashed biochar (DAB) in combination with carboxymethyl cellulose (CMC) presents an effective approach for addressing this problem. DAB improves soil structure by increasing porosity and water retention and enhancing plant nutrient utilization efficiency. The CMC provides advantages to plants by enhancing soil water retention, improving soil structure, and increasing moisture availability to the plant roots. The present study was conducted to investigate the effects of DAB and CMC amendments on maize under field capacity (70 FC) and drought stress. Six different treatments were implemented in this study, namely 0 DAB + 0CMC, 25 CMC, 0.5 DAB, 0.5 DAB + 25 CMC, 1 DAB, and 1 DAB + 25 CMC, each with six replications, and they were arranged according to a completely randomized design. Results showed that 1 DAB + 25 CMC caused significant enhancement in maize shoot fresh weight (24.53%), shoot dry weight (38.47%), shoot length (32.23%), root fresh weight (19.03%), root dry weight (87.50%) and root length (69.80%) over control under drought stress. A substantial increase in maize chlorophyll a (40.26%), chlorophyll b (26.92%), total chlorophyll (30.56%), photosynthetic rate (21.35%), transpiration rate (32.61%), and stomatal conductance (91.57%) under drought stress showed the efficiency of 1 DAB + 25 CMC treatment compared to the control. The enhancement in N, P, and K concentrations in both the root and shoot validated the effectiveness of the performance of the 1 DAB + 25 CMC treatment when compared to the control group under drought stress. In conclusion, it is recommended that the application of 1 DAB + 25 CMC serves as a beneficial amendment for alleviating drought stress in maize.

Enhancing maize growth through the synergistic impact of potassium enrich biochar and spermidine

Maize cultivated for dry grain covers approximately 197 million hectares globally, securing its position as the second most widely grown crop worldwide after wheat. Although spermidine and biochar individually showed positive impacts on maize production in existing literature, their combined effects on maize growth, physiology, nutrient uptake remain unclear and require further in-depth investigation. That’s why a pot experiment was conducted on maize with spermidine and potassium enriched biochar (KBC) as treatments in Multan, Pakistan, during the year 2022. Four levels of spermidine (0, 0.15, 0.30, and 0.45mM) and two levels of potassium KBC (0 and 0.50%) were applied in completely randomized design (CRD). Results showed that 0.45 mM spermidine under 0.50% KBC caused significant enhancement in maize shoot length (11.30%), shoot fresh weight (25.78%), shoot dry weight (17.45%), root length (27.95%), root fresh weight (26.80%), and root dry weight (20.86%) over control. A significant increase in maize chlorophyll a (50.00%), chlorophyll b (40.40%), total chlorophyll (47.00%), photosynthetic rate (34.91%), transpiration rate (6.51%), and stomatal conductance (15.99%) compared to control under 0.50%KBC validate the potential of 0.45 mM spermidine. An increase in N, P, and K concentration in the root and shoot while decrease in electrolyte leakage and antioxidants also confirmed that the 0.45 mM spermidine performed more effectively with 0.50%KBC. In conclusion, 0.45 mM spermidine with 0.50%KBC is recommended for enhancing maize growth.

Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model’s accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R2 of 0.567 and an RMSE of 5.598, and the model with AL had an R2 of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R2 = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model’s robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R2 = 0.792, RMSE = 2.8; CCC: R2 = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth.

Diagnosis of maize chlorophyll content based on hybrid preprocessing and wavelengths optimization

Leaf chlorophyll content (LCC) is one of important indicators for photosynthesis evaluation and crop nutrition diagnosis. For establishing an accuracy LCC diagnosis model, this study focused on reduce influences of multi-source interferences which resulted from external noises and internal multi-compositions. A systematic strategy with pipelines methods of hybrid preprocessing and wavelengths optimization was proposed to overcome such complicated interferences. Maize datasets of canopy reflectance and LCC measurements were collected under six fertilization treatments and three replications in three growth stages (V6, V9, and V12) in 2019 summer maize. Hybrid preprocessing was proposed by cascading preprocessing methods of discrete wavelet transformation (DWT) smoothing, multiplicative scatter correction (MSC), second derivative (D2). Optimization of sensitive wavelength was conducted to reduce the influences of multi-components overlapping. Methods of competitive adaptive reweighted sampling (CARS), iterative retaining informative variable (IRIV), and variable iterative space shrinkage space (VISSA) combined with partial least square (PLS) were used to explore and validate spectral responses traits of multi-components overlapping. The results of VISSA-PLS showed superior potentials for diagnose maize LCC with accuracy of RP2 of 0.76 and RMSEP of 2.45. The modeling results indicated that the systematic solution of DWT-MSC-D2-VISSA-PLS could improve the performance of LCC diagnosis. It provided application potentials for accurate diagnosis of maize growth status and lay foundations for topdressing recommendations.

Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems

Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m−2, 39 µmol/m−2, and 61.6 µmol/m−2, respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m−2 and 69.6 µmol/m−2, respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.

Exploring the utility of Sentinel-2 for estimating maize chlorophyll content and leaf area index across different growth stages

ABSTRACT This study investigated the utility of Sentinel-2 spectral data for estimating leaf area index (LAI), leaf and canopy chlorophyll content of maize at different growth stages. Vegetation indices based on the visible-near infrared and red-edge regions of the spectrum were computed from Sentinel-2 imagery acquired within one or two days of field data collection. Results showed that green chlorophyll index (CIgreen) and red-edge chlorophyll index (CIred-edge), using the red-edge variant centred at 705 nm, consistently showed higher relationship to maize LAI with r 2 of 0.65 and 0.63 during the early stages of growth, respectively, and an r 2 of 0.79 and 0.81 during tassel stage, respectively. Regarding canopy chlorophyll content the results indicated the spectral advantage of the Sentinel-2 sensor with the presence of two red-edge bands for continuous monitoring of maize chlorophyll content. Overall, the results indicated that maize biophysical variables can be monitored at satellite level using Sentinel-2 data.

Evaluating the sensitivity of water stressed maize chlorophyll and structure based on UAV derived vegetation indices

To further assess the sensitivity of crop chlorophyll and structure based on UAV vegetation indices (VIs) to maize water stress, a study was carried out in a maize field located in Inner Mongolia, China, with various levels of deficit irrigation over the entire 2018 and 2019 growing seasons. Ground measurements of stomatal conductance (Gs), leaf area index and leaf chlorophyll were used as references for maize water status, canopy structure and chlorophyll content, respectively. Four structure VIs and two chlorophyll VIs, and three regression algorithms (multiple linear, random forest and artificial neural networks regression) were adopted. The results showed that canopy structure derived from VIs had a significant correlation (p < 0.001) with Gs with the highest r value of 0.64 (n = 270) in 2018 and 2019. The transformed chlorophyll absorption in reflectance index, chlorophyll VI, could only estimate severe maize water stress with an r value of −0.47 (p < 0.001, n = 270) for the drier 2019. The water stress sensitivity of chlorophyll and structure VIs maybe significantly influenced by different responses of canopy structure and chlorophyll concentration to water stress, and the different spectral resolution of UAV multispectral cameras. Compared to non-linear machine learning regression algorithms, the multiple linear regression was robust enough to relate UAV-based multispectral VIs to Gs with coefficients of determination of 0.48 and 0.45 (n = 270) for 2018 and 2019, respectively. Although stable significant correlations were found between UAV multispectral VIs and Gs, annual changes in these specific expressions were also observed. Overall, our results demonstrated the potential of using structure VIs derived from UAV multispectral images and multiple linear regression approach to estimate maize water status in field scale.

Variation in Maize Chlorophyll Biosynthesis Alters Plant Architecture.

Chlorophyll is a tetrapyrrole metabolite essential for photosynthesis in plants. The first committed step of chlorophyll biosynthesis is catalyzed by a multimeric enzyme, magnesium chelatase, the subunit I of which is encoded by the oil yellow1 (oy1) gene in maize (Zea mays). A range of chlorophyll contents and net CO2 assimilation rates can be achieved in maize by combining a semidominant mutant allele of oy1 (Oy1-N1989) and a cis-regulatory modifier named very oil yellow1 (vey1) that varies between different inbred lines. We previously demonstrated that these allelic interactions can delay reproductive maturity. In this study, we demonstrate that multiple gross morphological traits respond to a reduction in chlorophyll. We found that stalk width, number of lateral branches (tillers), and branching of the inflorescence decline with a decrease in chlorophyll level. Chlorophyll deficit suppressed tillering in multiple maize mutants, including teosinte branched1, Tillering1, and grassy tillers1 In contrast to these traits, plant height showed a nonlinear response to chlorophyll levels. Weak suppression of Oy1-N1989 by vey1 B73 resulted in a significant increase in mutant plant height. By contrast, enhancement of the severity of the Oy1-N1989 phenotype by the vey1 Mo17 allele resulted in reduced plant height. We demonstrate that the effects of reduced chlorophyll contents on plant growth and development are complex and depend on the trait being measured. We propose that the lack of chlorophyll exerts growth control via energy balance sensing, which is upstream of the known genetic networks for branching and architecture.

Effect of Foliar and Soil Fertilization with New Products Based on Calcinated Bones on Selected Physiological Parameters of Maize Plants

This manuscript presents the effect of foliar and soil fertilizer produced from thermally processed bone waste on the initial growth and development of maize plants. The developed fertilizers were tested in three different doses in a pot experiment. Because nutrient deficiency interferes with plant physiological processes, the impact of the developed fertilizers on gas exchange parameters, relative chlorophyll content, and chlorophyll fluorescence parameters were assessed. Based on the conducted research, it was found that fertilization with developed foliar and soil fertilizers increased the relative content of chlorophyll in maize leaves and increased the value of gas exchange parameters and chlorophyll fluorescence. All determined parameters of gas exchange and chlorophyll fluorescence showed a positive correlation depending on the dose of foliar fertilization used (average value r = 0.8414). In turn, the soil fertilization that was utilized during the experiment significantly correlated only with the content of chlorophyll (r = 0.6965). The tested fertilizers improvement of the physiological parameters of the plants, which indicates the fertilizing efficiency of the tested fertilizers.

Multi-Spatial Resolution Satellite and sUAS Imagery for Precision Agriculture on Smallholder Farms in Malawi

A collection of spectral indices, derived from a range of remote sensing imagery spatial resolutions, are compared to on-farm measurements of maize chlorophyll content and yield at two trial farms in central Malawi to evaluate what spatial resolutions are most effective for relating multispectral images with crop status. Single and multiple linear regressions were tested for spatial resolutions ranging from 7 cm to 20 m using a small unmanned aerial system (&lt;small&gt;sUAS&lt;/small&gt;) and satellite imagery from Planet, &lt;small&gt;SPOT&lt;/small&gt; 6, Pléiades, and Sentinel-2. Results suggest that imagery with spatial resolutions nearer the maize plant scale (i.e., 14–27 cm) are most effective for relating spectral signals with crop health on smallholder farms in Malawi. Consistent with other studies, green-band indices were more strongly correlated with maize chlorophyll content and yield than conventional red-band indices, and multivariable models often outperformed single variable models.

Evaluation of the potential of jack bean [Canavalia ensiformis (L.) DC.] and velvet bean [Mucuna pruriens (L.) DC.] aqueous extracts as post-emergence bio-herbicides for weed control in maize (Zea mays L.)

The objective of the study was to evaluate the effect of jack bean and velvet bean aqueous extracts when applied post-emergence alone or in combination with half dosage rates of atrazine on goosegrass, beggarticks, and maize. Three potted experiments with fifteen treatments were laid out in a completely randomized design replicated four times and repeated once. The bio-herbicidal treatments were applied to maize at two weeks after crop emergence and on the weeds at the 3-4 leaf stage. Results showed that maize height increased significantly (p<0.05) across all treatments from 24 to 34 days after crop emergence (DACE). Maize chlorophyll content significantly (p<0.05) increased from 24 to 34 DACE in all the treatments. In contrast, dry weight, height, and chlorophyll content of goosegrass were significantly (p<0.05) reduced by all treatments. The height of beggarticks was not significantly (p>0.05) affected while chlorophyll content and dry weight were significantly (p<0.05) reduced by all treatments. All the treatments caused complete weed control at 6 and 10 DAS in beggarticks and goosegrass, respectively. It was concluded that velvet bean and jack bean aqueous extracts are phytotoxic to weeds of divergent morphology.

Detection of Chlorophyll Content in Maize Canopy from UAV Imagery

Chlorophyll is an important indicator for the evaluation of plant photosynthesis ability and growth status. In order to obtain the spatial distribution of chlorophyll content in field crops quickly and non-destructively, the chlorophyll content detection of maize canopy was carried out based on UAV image processing. In this paper, the RGB (red, green, blue) images of the maize canopy were measured in the Hengshui, Hebei province. The processing method was proposed to estimate the chlorophyll content in the field. Firstly, the image was segmented based on the HSV (hue, saturation, value) color model to remove soil background. The parameters were extracted related to the color feature and the texture feature in the image. On the one hand, there were 10 color parameters were involved including the red, green, blue, green and red differences, normalized red and green differences, and so on. On the other hand, the texture parameters were calculated with mean, standard deviation, smoothness, third moment, etc. The detection model of maize chlorophyll content was established and discussed based on BP neural network. The experiment results showed that: (1) The detecting accuracy of chlorophyll content was increased by the image parameter combination of color and texture features. Compared with the color feature, the determination coefficient of the model was increased from 0.6987 to 0.7246 by involving the texture feature. (2) The segmentation of canopy could help to improve the estimation accuracy due to the influence elimination of soil background, and the determination coefficient of model increased from 0.7246 to 0.7564, meanwhile, the root mean square error (RMSE) decreased from 4.4659 mg·L-1 to 4.4425 mg·L-1. The chlorophyll content of maize canopy was calculated at pixel level to indicate the field statues. The distribution map of chlorophyll content in field maize canopy was drawn based on pseudo-color technique. It provided a tool to visually distinguish the field road and canopy area, showing the difference in chlorophyll distribution of the plot. The UAV imagery could help to measure the content and distribution of maize chlorophyll non-destructively, and provide a support for crop evaluation and precision management in the field.

Photosynthetic pigments accumulation and some growth indices of cowpea, maize and tomato in response to interspecific and intraspecific competition stress

This study aimed at investigating the photosynthetic pigment accumulation and some growth indices of cowpea, maize and tomato in response to interspecific and intraspecific competition stress. The study was carried out under a screen-house to minimize extraneous factors such as pests and rodents using a randomized complete block design (RCBD). Seeds of cowpea, maize and tomato were collected from the Department of Crop Production and Protection, Faculty of Agriculture, Obafemi Awolowo University, Ile Ife, Osun state, Nigeria. These seeds were planted at a depth of about 3 mm below the soil. The seeds were sown at the rate of six seeds per pot in the monoculture, while in the pots designed for the mixed culture of maize and cowpea, maize and tomato, cowpea and tomato, three seeds of each plant were sown. Two seeds of each plant were sown in the pots with the three crops. The treatments were then supplied with 500 mL of tap water in the morning and in the evening respectively until the seedlings become fully established. The photosynthetic pigments were determined spectrophotometrically with three replicates. Plant growth indices were determined according to Hunts (1978) using leaf area and dry matter data collected at four and six weeks after planting. Statistical analysis was performed using statistical analytical software SAS version 9.2. The results indicated that photosynthetic pigments accumulation (Chlorophyll a, b and carotenoid) in maize (15.98, 23.92 and 44.72 μM), Chlorophyll b and carotenoid in tomato plants (12.48 and 1,178.7 μM) in the sole stands were more than the mixed culture of maize with cowpea and tomato (7.195, 14.74 and 0.00 μM). Also, total Chlorophyll in maize (1,127.8 μM), Chlorophyll a and total Chlorophyll in tomato (3.95 and 1,317.5 μM) in the mixed culture were more than in the sole culture of maize (1,030.9 μM) and tomato plants (−9.40 and 546.3 μM). The different photosynthetic pigments accumulated in cowpea were greatly enhanced in the mixed culture than in the sole culture. All the photosynthetic pigments of maize, cowpea and tomato in all the treatments analysed in this study were significantly different at P >0.05. Plant growth indices such as net assimilation rate, relative growth rate, crop growth rate and tissue water contents of these plants had higher value in the intercropped than the check crop (sole). These plant growth indices were significantly different to one another at P >0.05. This study concluded that competition for shared resources in the mixed culture of tomato, maize and cowpea enhanced growth and accumulation of photosynthetic pigments.

Effects of tetrabromobisphenol A on maize (Zea mays L.) physiological indexes, soil enzyme activity, and soil microbial biomass.

TetrabromobisphenolA (TBBPA) is the most widely used brominated flame retardant, and it has the characteristics of persistent organic pollutants (POPs), attracting considerable attention. Many studies mainly focus on TBBPA toxicological effects in aquatic animals and rodents, but the ecotoxicology data of TBBPA on plant-soil system are limited so far. In this study, we assessed the impacts of TBBPA on maize (Zea mays L.) physiological indexes, soil enzyme activity, and soil microbial biomass at different concentrations of TBBPA (0, 0.75, 3.75, 7.5, 15, 37.5 and 75 mg·kg-1) and explored their relationships. Results showed that the maize physiological indexes and chlorophyll contents were significantly decreased by TBBPA, the activities of anti-oxidative enzymes including catalase (CAT), peroxidase (POD) and polyphenol oxidase (PPO) and the contents of malondialdehyde (MDA) were remarkably enhanced. Meanwhile, TBBPA activated the CAT, POD and PPO activities in soil. The low concentrations TBBPA promoted the activities of soil urease (S-UE), neutral phosphatase (S-PE) and increased the soil microbial biomass carbon (SMBC) and nitrogen (SMBN) while the high concentrations TBBPA suppressed them. Notably, the data indicated microbial biomass had respectively a significant correlation with CAT, PPO and S-UE in soil in the presence of TBBPA, and maize chlorophyll contents were associated with SMBN, CAT, and PPO. Taken together, TBBPA caused soil pollution, affected soil enzyme activities and microbial biomass, and hindered maize growth under the current experimental condition, suggesting the interactions among maize growth, soil enzyme, soil microorganism in maize rhizosphere of TBBPA-polluted soils are very important aspects to comprehensively evaluate the ecotoxicological effects of TBBPA.

Assessment of Leaf Color Chart Observations for Estimating Maize Chlorophyll Content by Analysis of Digital Photographs

Developed as a nondestructive aid for estimating the N content in rice (Oryza sativa L.) crops, leaf color charts (LCCs) are a numbered series of plastic panels that range from yellow‐green to dark green. By visual comparison, the panel value closest in color to a leaf indicates whether N is deficient, sufficient, or in excess. Because the selected values depend on subjective decisions by an observer, our goal was to determine whether spectral reflectances or digital color photographs could provide an objective, reproducible, and potentially automated method for determining LCC values. Maize (Zea mays L.) leaves were collected on two dates from an ongoing N fertilization experiment. Observed LCC panel values of selected leaves were highly correlated to chlorophyll content and chlorophyll meter values. Spectral reflectances and digital photographs were analyzed to predict the LCC panel value that was observed for each leaf. Supervised classifications of digital photographs using minimum distance provided reasonable predictions of the LCC value, but the spectral angle mapper did not. The dark green color index and the triangular greenness index predicted LCC panel values well. Uncorrected digital photographs (raw) produced better agreement with observed LCC panel values when using spectral indices, whereas color‐corrected photographs (JPEG) produced better agreement using the supervised classification methods. We concluded that subjective visual observations using a LCC were not worse than the objective methods for estimating leaf chlorophyll content. Visual observations using LCCs may be an easy‐to‐use and low‐cost method for managing N at smaller scales.

Effects of straw mulching on maize photosynthetic characteristics and rhizosphere soil micro-ecological environment

Straw mulching is an effective measure to improve soil properties, crop growth, and yield. To further understand the advantage mechanisms of straw mulching, a field experiment with seven straw mulching levels (0 to 18 000 kg ha-1) was conducted to study the effects of straw mulching on maize (Zea mays L.) photosynthesis and rhizosphere soil micro-ecological environment. Results showed that maize chlorophyll content was evidently affected by straw mulching, and the highest chlorophyll content was at 12 000 kg ha-1 (M4). Straw mulching could significantly improve the photosynthetic characteristics of maize, and the difference between M4 and 0 kg ha-1 (M0) was significant. There was as trend change in soil microbe quantity; it first increased and then decreased with increasing straw mulching levels, and the most suitable straw mulching level for different types of microorganisms was 9000 kg ha-1 (M3) or M4. Straw mulching significantly enhanced soil enzyme urease, invertase, dehydrogenase, and protease activities, but when the straw mulching level reached a certain level, the effect of straw mulching was no longer apparent and even had some adverse effects at straw mulching levels higher than 15 000 kg ha-1 (M5). Yield in M4 (10 186.84 kg ha-1) was the highest compared with M0 (9365.12 kg ha-1), and yield significantly increased by 8.8%. Correlation analyses indicated that the soil microbe quantity and aforementioned enzyme activities were all significantly and positively correlated with maize chlorophyll content, photosynthetic rate, and yield. Findings suggest that straw mulching can apparently increase soil microbe quantity and enzyme activities and improve crop photosynthesis and yield; the M4 level is the most reasonable straw mulching level in this study under comprehensive consideration, and a straw mulching level that is too high (over M5) will have some negative effects.

Using a Simple Leaf Color Chart to Estimate Leaf and Canopy Chlorophyll a Content in Maize (Zea mays)

This study utilized a leaf color chart (LCC) to characterize the variation in leaf chlorophyll and estimate canopy chlorophyll in maize (Zea mays). The LCC consisted of four levels of greenness and was used to sort maize leaves in 2011 for three fields near Mead, Nebraska, USA. Leaf chlorophyll content for each color chart class was determined using two leaf-level sensors. The variation within each LCC class was reasonable (CV < 56%). The darkest color class predominated and indicated adequate fertilization rates using a Minolta SPAD-502 meter. Canopy chlorophyll content was estimated using destructively measured leaf area index (LAI) and the LCC. This approach was verified with a method utilizing canopy reflectance collected by both satellite imagery and a four-band radiometer. The error between the two methods was reasonable (RMSE = 0.55–0.88 g m−2; CV = 25.6–50.4%), indicating that both leaf and canopy chlorophyll can be estimated cheaply without a wet lab or field-based sensors.

Enhanced salt tolerance in maize plants induced by H2O2 leaf spraying is associated with improved gas exchange rather than with non-enzymatic antioxidant system

Hydrogen peroxide (H2O2) is an essential signaling molecule that mediates plant responses against several biotic and abiotic stresses. H2O2 pretreatment has emerged as a signaling way, inducing salt stress acclimation in plants. Here, we analyzed the effects of H2O2 leaf pretreatment on the non-enzymatic defense system (ascorbate and glutathione), plant growth, relative water content (RWC), relative chlorophyll content, H2O2 content, and gas exchange in maize plants under NaCl stress. The results showed that salinity reduced the leaf area and shoot and root dry mass as compared to control, and the leaf spraying with H2O2 significantly improved the growth of salt stressed plants. Photosynthesis and transpiration, stomatal conductance and intercellular CO2 concentration were strongly decreased by salinity after 7 and 14 days of salt exposure; however, the decrease was lower in plants sprayed with H2O2. The improved gas exchange in H2O2-sprayed stressed plants correlated positively with higher RWC and relative chlorophyll content and lower leaf H2O2 accumulation under NaCl stress conditions. Ascorbate and glutathione did not play any obvious effects as non-enzymatic antioxidants in the ROS scavenging. In conclusion, the salt tolerance induced by H2O2 leaf pretreatment is attributed to a reduction in the H2O2 content and maintenance of RWC and chlorophyll in maize leaves. These characteristics allow maize plants to maintain high rates of photosynthesis under salt stress and improve the growth.

Deviation analysis of vehicle-borne prediction system for maize chlorophyll content based on matrix transformation

Deviation analysis of vehicle-borne prediction system for maize chlorophyll content based on matrix transformation