Leaf Chlorophyll Content Index Research Articles

Superhydrophobic sand mulch and date palm biochar boost growth of Moringa oleifera in sandy soils via enhanced irrigation and nutrient use efficiency

IntroductionIt is desirable to rehabilitate desert ecosystems with a selection of native plant species that render ecosystem services and yield natural products for creating a high-value industry, e.g., pharmaceuticals or cosmetics. However, plant growth under arid and hyper-arid conditions, such as in the Arabian Peninsula, is constrained by heat, freshwater scarcity, and alkaline sandy soils with low nutrient and water holding capacity. Therefore, it is imperative to develop nature-based sustainable technologies to improve arid soil conditions, as well as increase irrigation and nutrient-use eficiency.MethodsHere, we report on a study evaluating the effects of two complementary soil amendment technologies, namely Superhydrophobic sand (SHS) mulch and engineered biochar (EB) on the growth of Moringa oleifera plants. Effects of SHS (1cm-thick), EB (2% w/w), and SHS+EB treatments were tracked in greenhouse plants under normal (N, 100% field capacity) and reduced (R, 50% of N) irrigation scenarios for over 150 days, where EB treatments were pre-loaded with nutrients and remaining treatments received traditional NPK fertilizer.ResultsSignificant benefits of the SHS, EB, and SHS+EB treatments were found in terms of increased plant height, trunk diameter, leaf area, leaf chlorophyll content index, stomatal conductance, and shoot and root biomass in comparison with the controls. Evaporation water savings due to SHS mulching significantly enhanced transpiration under N and R scenarios. Similarly, EB and SHS+EB treated plants experienced higher transpiration than in the control plants under N and R conditions (p< 0.05). In response to water stress due to excessive evaporation, metabolomics analysis showed a higher accumulation of amino acids in control plants than other treatments under both irrigation regimes. Meanwhile, a higher abundance of sugars (i.e., D-Mannose, D-Fructose, glucose) and organic acid (i.e., malic acid) was observed in SHS and EB-treatments for Variable Importance in Projection (VIP) scores >1.0 (i.e., the scores considered of significance in contributing to the differences between treatment groups).DiscussionThe results show the synergistic benefits of SHS and EB technologies for addressing the challenges of water scarcity and nutrient limitation in arid regions, which couldcontribute to the success and sustainability of agriculture and greening efforts in such regions.

Inversion of biophysical parameters of potato based on an active learning pool-based sampling strategy

ABSTRACT Timely estimations of leaf chlorophyll content (LCC) and leaf area index (LAI) can provide critical information for potato field management. We employed a hybrid method that integrated machine learning with a radiative transfer model to estimate potato growth parameters. A look-up table was generated using the PROSAIL model, which was used as an unlabelled sample set. Measurements were taken from a potato field, and the data were labelled according to growth period and variety. Then, training samples for potato LCC, LAI, and canopy chlorophyll content (CCC) were selected from the simulated unlabelled sample set using the Euclidean distance-based diversity algorithm based on different labelled data sets. The training sample size required to accurately estimate the parameters varied considerably depending on the parameter, variety, and growth period, despite using the same labelled data set. Moreover, our results indicate that the growth period has a substantial impact on model accuracy and needs to be considered when constructing the labelled data set. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato parameter estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

Pre-Crop and Residue Management Effects on Photosynthesis Efficiency and Grain Yield of Dryland Wheat Genotypes

To evaluate dryland wheat genotypes’ performance under different pre-crop and residue managements under dryland conditions, a split–split plot experiment based on the RCB design, with three replications, was conducted for two years (2017–2018 and 2018–2019). The site of the study has a long-term average precipitation, temperature, and relative humidity of 376 mm, 9 °C, and 50%, respectively. Wheat–wheat and vetch–wheat cropping systems were considered in the main plots, different wheat and vetch residue levels, including 0, 2, and 4 t ha−1, were located in the subplots, and five dryland wheat genotypes, including Sadra, Hashtroud, Baran, Varan, and Ohadi, were allocated in the sub-sub plots. The results indicated that the leaf chlorophyll content index (CCI) and stomatal conductance (gs) were greater in the vetch–wheat cropping system compared to the wheat monoculture system for all genotypes. The normalized difference vegetation index (NDVI) of the genotypes improved by applying the crop residue. Over two years, the application of crop residues resulted in higher variable fluorescence at the J and I steps, as well as an increase in the photosynthesis performance index (PI). The Varan and Baran genotypes stood out as the superior genotype, exhibiting the highest values in physiological characteristics and grain yield under the application of 4 t ha−1 of vetch residue. The grain-filling rate (GFR) was reduced, while the grain-filling duration (GFD) was increased with increasing the crop residue levels. The enhanced grain yield of the wheat genotypes grown under vetch residue was attributed to factors such as improvement in leave pigments and photosynthetic efficiency, which facilitate longer grain filling duration, with high grain weight. As a result, it is advisable to adopt a vetch–wheat cropping system with a high proportion of crop residue in dryland regions to achieve increased and sustainable wheat production.

Irrigation and oil palm empty fruit bunch mulch enhance eggplant growth, radiation interception and dry matter yield

Organic mulching is a well-known management practice that conserves soil water and nutrients as well as increases crop yield. Nonetheless, research on combined organic mulching using oil palm empty fruit bunch (EFB) and irrigation is limited. Field-based experiments were conducted over three seasons to test the sole and combined effects of EFB as organic mulch and irrigation on the growth, total dry matter yield (TDMY), accumulated intercepted photosynthetically active radiation (AIPAR), and radiation use efficiency (RUE) of African eggplant (Solanum aethiopicum L.) in a low fertile tropical sandy clay loam soil. Air-dried EFB was used as an organic mulch by spreading it on the soil surface at rates of 0 (EFB0), 20 (EFB20), and 40 t ha−1 (EFB40), and either fully-irrigated (I100), deficit-irrigated (I40), or non-irrigated (I0). The I100 plots were irrigated to field capacity (FC) every 3–4 days based on PR2 Profile Probe measurements and the resultant irrigation volume supplied to the plants via drip irrigation tubes. The I40 plots received 40 % of the water given to the I100 plots, and the I0 plots were solely rain-fed. At the end of the third season, the 40 t ha−1 EFB-mulch increased soil pH, electrical conductivity (EC), soil organic carbon, potassium, cation exchange capacity, and the soil’s specific surface area. In the first season, all the measured eggplant growth and yield parameters were neither responsive to irrigation only, EFB-mulch only, or both. In the second and third seasons, the EFB20 and EFB40 treatments significantly (p < 0.05) increased leaf chlorophyll content index (LCCI), photosystem II (Fv/Fm ratio), absolute performance index (PIabs), TDMY, AIPAR, and RUE compared to the non-mulched control treatment. Soil pH was high in the EFB-mulched plots and correlated positively with TDMY and AIPAR. The I100 significantly improved LCCI, Fv/Fm, PIabs, and TDMY during the second season. In the third season, a highly significant interaction between irrigation and mulching was detected on TDMY, AIPAR, RUE, LCCI, Fv/Fm ratio, and pH (H2O). This indicated a positive effect on soil nutrient availability especially phosphorus as TDMY and AIPAR correlated with soil pH. The I100 and I40 significantly increased AIPAR by 48.1 % and 37.2 %, and RUE by 26.7 % and 11.0 %, respectively, compared to I0 during the third season. The total dry matter yield of the African eggplant was enhanced by EFB-mulch, with the effect increasing over up to three growing seasons, especially when combined with irrigation during dry periods.

Effect of Powdery Mildew on the Photosynthetic Parameters and Leaf Microstructure of Melon

Powdery mildew is a fungal disease devastating to crops, causing significant quality and yield loss. As one of the most important fruits in the world, melon also is damaged by powdery mildew. The present study investigated the effect of powdery mildew on the photosynthetic parameters and leaf microstructure of melons, the ultrastructure of the leaf surface, photosynthetic index, chlorophyll content, yield, and quality index of five thick-skinned and differently shaped melon varieties. The net photosynthetic rate, transpiration rate, leaf water use efficiency, and chlorophyll levels were significantly (p &lt; 0.05) higher in Kangbing F3800 plants compared to the other four varieties. In the case of powdery mildew infection, the total number of stomata in the upper and lower epidermis was particularly high in the Zhongtian No. 8 and Zhongtianxueqiong varieties, respectively. The stomatal length and width were highest in the upper epidermis of Zhongtian No. 12 leaves and in the lower epidermis of Zhongtian No. 8 leaves compared to the other varieties. The total yield and meat thickness were significantly (p &lt; 0.05) higher in the Zhongtianxueqiong variety than the others, along with the low edge sugar content. Overall, powdery mildew impacted differently the photosynthetic and leaf surface characteristics of the five melon varieties. Kangbing F3800 emerged as the most resistant variety, making it the preferred choice for introducing and promoting thick-skinned melon varieties in the Ningxia Hui Autonomous Region of China.

Improving Water Use Efficiency and Economic Benefits of Cropping System Through Intercropping in an Arid Climate

The sustainable increase of total productivity by improving resources use efficiency in arid agricultural farming areas is crucial, and intercropping may be a good practice to be implemented in these arid regions. For this purpose, a three-year field experiment was conducted as a randomized complete block design (RCBD) with three replications at the research farm of the Agricultural and Natural Resources Research Station of Gonabad, Gonabad, Iran to assess the agronomic and economic indices of intercropping patterns. The experiment treatments included C1: sole cotton, C2: sole sesame, and intercropping ratios (C3: 20:80, C4: 40:60, C5: 50:50, C6: 60:40, and C7: 80:20 cotton-sesame ratio). The results showed that the leaf chlorophyll content and leaf area index were significantly higher in the intercropped plants compared to the sole cropped plants. The yield components of both crops (such as branches per plant, capsules per plant, seeds per capsule, and 1000-seed weight for sesame, and opened bolls per plant, closed boll per plant, and seed cotton per boll for cotton) significantly improved under intercropping. However, the highest sesame seed yield (2703, 1979, and 1358 kg ha-1, respectively) and seed cotton yield (3749, 2179, and 3426 kg ha-1, respectively) in the three experiment years were observed in the sole cropping treatment. The implementation of intercropping significantly improved the water use efficiency of the cropping system, so that the highest values in the first to third year (0.67, 0.51, and 0.41 kg m-3, respectively) were recorded in the C4, C3, and C7 treatments. The intercropping evaluation indices revealed the advantage of intercropping compared to the sole cropping. The highest value of the land equivalent ration in the first year (1.28) belonged to the C4 treatment, while in the second and third years, belonged to the C7 treatment (1.40 and 1.10, respectively). The calculation of the aggressivity index revealed that in most of the intercropping patterns, especially in the first and second years, cotton showed greater competitive ability than sesame. The highest actual yield loss value in the first year (0.64) belonged to the C3 treatment, while in the second and third years, belonged to the C7 treatment (1.42 and 0.34, respectively). The highest economic advantage in terms of the monetary advantage index in the first year was obtained by the C4 treatment (1140.5), and in the second and third years, was observed in the C7 treatment (940.6 and 265.5, respectively). The intercropping advantage index in the three experiment years was highest (1.41, 3.38, and 0.80, respectively) for the C7 treatment. Eventually, the results of this research show that cotton and sesame are able to adapt well to the intercropping and this cropping system can significantly improve the resources use efficiency (especially water and land) in an arid area enjoying greater economic benefit than sole cropping

Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize

This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both remote RGB images and ground hyperspectral sensor data. Physiological parameters (i.e., leaf area index, relative water content, leaf chlorophyll content index, and gas exchange parameters) were measured. Correlation and multivariate data analysis (principal component analysis and stepwise linear regression) were performed to assess the strength of the relationships between eco-physiological measured variables and both RGB indices and hyperspectral data. The results revealed that the red-edge indices including CIred-edge, NDRE and DD were the best predictors of the maize physiological traits. In addition, stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature. Among the RGB indices, the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content. Moreover, the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level. The findings confirmed the capacity of selected RGB and hyperspectral indices to evaluate the water and nitrogen status of sweet maize and provided opportunity to expand experimentation on other crops, diverse pedo-climatic conditions and management practices. Hence, the aerially collected RGB vegetation indices might represent a cost-effective solution for crop status assessment.

Estimation of grain filling rate of winter wheat using leaf chlorophyll and LAI extracted from UAV images

The grain filling rate (GFR) plays a pivotal role in assessing winter wheat grain development and guiding crop variety selection and cultivation strategies. Presently, GFR monitoring primarily relies on destructive sampling methods, which are impractical for swift assessments in extensive agricultural fields. Our study introduces a novel approach for estimating winter wheat grain filling rate (GFR) using leaf chlorophyll content (LCC) and Leaf Area Index (LAI) extracted from UAV imagery. Initially, we established a quantitative relationship between GFR and LAI as well as LCC in winter wheat. Subsequently, UAV multispectral imagery was employed to track the time-series dynamics of LAI and LCC during the grain-filling stage. Ultimately, we constructed an estimation model for winter wheat GFR and achieved plot-scale GFR mapping through UAV-based imaging technology. To identify sensitive vegetation index combinations for LAI and chlorophyll content (SPAD), we employed adaptive re-weighted sampling and continuous projection algorithms, respectively. Following this, we employed partial least squares and random forest algorithms to develop LAI and SPAD estimation models, which were then compared to full-band regression. Finally, GFR estimation was based on the conversion models of LAI and SPAD to GFR. Our findings revealed that, despite variations in water supply, there were no significant differences in GFR trends among the irrigated treatments. However, in the nitrogenous fertilizer application treatments, GFR initially declined with increased nitrogenous fertilizer supply, reaching its peak between 21 and 25 days post-flowering, during which period the differences in GFR among the various nitrogenous fertilizer application treatments were not significant. As the reproductive period advanced, higher nitrogenous fertilizer treatments maintained a higher GFR for a more extended period and experienced a slower decline compared to the lower nitrogenous fertilizer treatments. LAI validation yielded an R2 of 0.84, a root mean square error (RMSE) of 0.39, and a normalized root mean square error (NRMSE) of 10.9%. SPAD validation resulted in R2, RMSE, and NRMSE values of 0.79%, 1.30%, and 12.5%, respectively. The GFR estimation achieved an R2 of 0.77, an RMSE of 0.32 mg/d, and an NRMSE of 11.3%. Mapping the GFR distribution in the field, coupled with water and fertilizer management, supports UAVs in monitoring crop growth and refining field-level water and fertilizer decisions.

Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data

Nitrogen is an essential nutrient in crop growth cycle and directly affects the photosynthesis of crops. The leaf chlorophyll content (LCC) and leaf area index (LAI) are important for characterizing photosynthetic capacity of crops and are critical indicators for diagnosing nitrogen status of crops. Hyperspectral remote sensing technology provided a means to achieve crop LCC and LAI estimation. However, the redundancy of spectral data and canopy structure effect can cause poor robustness of the estimating models, further hindering the development and application of estimating models across different crop species. In this study, a method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops. First, for the spectral dataset generated by the PROSAIL model, a deep neural network was used to construct LCC and LAI estimation models (called DNNCA model). Secondly, for the hyperspectral data collected from field crops (soybean, canola and wheat), incremental learning using regularization (LwF algorithm) was used to update the DNNCA model parameters. Finally, the dilution curve model based on the LCC-LAI anisotropic growth relationship was developed to assess crop nitrogen status. The results showed that: (1) The constrained bi-objective optimizated DNNCA model can consider the interactive effect of LAI and LCC on spectral reflectance, and achieved reliable estimation on PROSAIL simulation data set (LAI:R2 = 0.82, RMSE = 0.77 m2/m2; LCC:R2 = 0.91, RMSE = 6.4 ug/cm2). (2) By incremental learning, DNNCA model has continuous learning capability and stable estimation on cross-crop (canola, soybean and wheat) field-measured data (LAI:R2 = 0.64–0.82, RMSE = 0.58–1.02 m2/m2; LCC:R2 = 0.56–0.82, RMSE = 3.9–10.5 ug/cm2). (3) The relationship between the NNILCC and the NNILNC was significant. The NNILCC derived from the anisotropy relationship between crop LAI and LCC was an effective tool for crop nitrogen status diagnosis. (4) The process of crop LAI, LCC and NNILCC reflected the effect of water and nitrogen supply on crop growth. The appropriate water-nitrogen treatments contributed to LAI increase and LCC accumulation. The study demonstrated that hyperspectral remote sensing technology combined with incremental learning is an effective method for cross-crop growth monitoring and nitrogen diagnosis. These results provide a reference and basis for filed water-nitrogen supply and management.

Estimating Maize Maturity by Using UAV Multi-Spectral Images Combined with a CCC-Based Model

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R2 value was 0.704, the RMSE was 34.58 μg/cm2, and the MAE was 26.27 μg/cm2. The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R2 values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale.

Estimation of Sensor-based site specific variable rate fertilizer application for maize (Zea mays L.) crop

Optical spectrometry sensors in crops offer a remarkable technological breakthrough in the field of variable-rate nitrogen fertilization. A field study was conducted during rainy (kharif) season of 2021 at the research farm of the Agricultural Engineering College and Research Institute Tamil Nadu Agricultural University Coimbatore to estimate maize crop nitrogen (N), Normalized Difference Vegetation Index (NDVI) value and chlorophyll content in hybrid maize COH (M) 8. Fertilizers were administered to the plots following the recommendations (250:75:75 kg NPK ha-1) given under Soil Test Crop Response, with a goal yield of 9t ha-1 predicted based on the initial soil available N, P, and K values. The experimental findings revealed a significant impact of nitrogen rate (P&lt;0.001) on the percentage of nitrogen content in the leaves (% N leaf content). Additionally, there was a decrease in maize leaf chlorophyll content index over time, with ranges of 32.96 to 50.57, 28.78 to 41.78, 24.81 to 35.86, 22.12 to 28.54, and 14.34 to 20.56. On the contrary, the NDVI experienced an increase throughout the season, with ranges of 0.32 to 0.49, 0.30 to 0.55, 0.28 to 0.66, 0.46 to 0.88, and 0.56 to 0.84. The study will help foster sustainability within modern intensive farming practices by emphasizing the importance of reducing environmental pollution caused by applying Sensor-based site-specific nitrogen fertilizer for maize crop.

Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean.

Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.

Impact of organic fertilization by the digestate from by-product on growth, yield and fruit quality of tomato (Solanum lycopersicon) and soil properties under greenhouse and field conditions

BackgroundThe application of organic fertilizer is a sustainable approach to maintain soil fertility in agricultural crop production. In contrast to other organic fertilizers, the digestate from by-products of anaerobic digestion has not been well characterized in terms of its agronomic properties. In this study, different fertilization treatments were investigated to evaluate their impacts on growth, yield and fruit quality of tomatoes and on soil properties under greenhouse and field conditions. The experiments comprised a control (unfertilized) and three treatments with the same nitrogen dose: chemical fertilizer, digestate from by-product (organic fertilizer) and digestate combined with chemical fertilizer.ResultsThe results showed that the application of digestate significantly increased the growth and fruit quality of tomato including height, stem diameter, leaf chlorophyll content index, and photosynthetic rate of tomato plant and sugar–acid ratio, protein content, and ascorbic acid content of the fruit. The nitrate contents in tomato fruit were lower in the digestate treatment and digestate combined with chemical fertilizer treatment than in the chemical fertilizer. The digestate combined with chemical fertilization resulted in the greatest increase in tomato yield, up to 26.29% and 10.78% higher than that in the chemical fertilizer treatment under field and greenhouse conditions, respectively. Moreover, fertilization with digestate treatment and digestate combined with chemical fertilizer treatment increased soil fertility, including soil nitrogen and carbon contents, and enhanced soil enzyme activities under both field and greenhouse conditions. In addition, the growth, yield, and fruit quality of tomato were significantly correlated with soil chemical characteristics and soil enzyme activities.ConclusionsThe effects of digestate treatments to maintain a stable tomato yield and improve fruit quality may be due to the enhanced soil enzymatic activities and chemical properties. These results suggest that the use of digestate as a full or partial replacement for chemical fertilizer could improve the growth and fruit quality of tomato, maintain the yield, and reduce the use of inorganic fertilizers in tomato production.Graphical

Growth response of not-ready-to-distribute tea (Camellia sinensis (L) o. Kuntze) seedlings due to application of biofertilizer at various concentrations and intervals

The plantation rejuvenation program makes the need for ready-to-distribute tea seedlings getting higher. Meanwhile, the nursery's seedlings are mostly not ready to distribute. This study aimed to determine the response of not-ready-distribute tea seedlings due to the application of biofertilizers at various concentrations and intervals. This experiment was carried out at Gambung Tea and Quinine Research Center Nursery, conducted from December 2021 to February 2022 at an altitude of 1,350 meters above sea level (asl). The experimental method used was a randomized block design with eight treatment combinations, namely: control (no fertilizer); urea fertilizer every two weeks; Biofertilizer 5 mL L-1 + interval once a week; Biofertilizer 5 mL L-1 + interval every two weeks; Biofertilizer 10 mL L-1 + break once a week; Biofertilizer 10 mL L-1 + interval every two weeks; Biofertilizer 15 mL L-1 + interval once a week; Biofertilizer 15 mL L-1 + interval every two weeks, all repeated four times. The experimental results showed that treatment of biological fertilizers influenced the parameter number of leaves and chlorophyll index. The application of biofertilizer with a concentration of 15 mL L-1 + interval of 2 weeks greatly influenced the parameters of leaf number and chlorophyll content index.

Sorghum growth influenced by cultivar, nitrogen fertilizer rate and plant density under different environmental conditions

This study aimed to determine the effect of cultivar, nitrogen fertilizer rate and plant density on sorghum growth performance under different environmental conditions. A field experiment was carried out at two different locations of North-West province during 2016/17 and 2017/18 planting seasons. The experiment was laid out as a split split plot arrangement fitted into a randomized complete block design with four replications. The main plot factors were higher plant density (33 333 plants/ha) and lower plant density (22 222 plants/ha). The subplot factors consisted of nitrogen fertilizer rates of 0, 100 and 150 kg/ha. The sub-sub plot factors included two sorghum cultivars, PAN 8625 and PAN 8816. The measured growth parameters were plant height, stem diameter, leaves per plant, leaf chlorophyll content and leaf area index. Sorghum fertilised with 100 kg N/ha had a significantly taller plant of 94.07 cm than sorghum fertilised with 0 and 150 kg N/ha at 51 DAP. During 2016/17 planting season, sorghum cultivar PAN 8625 had a significantly higher number of 13.29 leaves than PAN 8816 at 51 DAP. Sorghum cultivar PAN 8625 also had a significantly higher number of 12.71 leaves than PAN 8816 at 74 DAP. Sorghum growth was enhanced by application of 100 kg N/ha. In this study, sorghum cultivar PAN 8625 is recommended to farmers due to its superior growth performance.

Analyzing Fire Severity and Post-Fire Vegetation Recovery in the Temperate Andes Using Earth Observation Data

In wildfire areas, earth observation data is used for the development of fire-severity maps or vegetation recovery to select post-fire measures for erosion control and revegetation. Appropriate vegetation indices for post-fire monitoring vary with vegetation type and climate zone. This study aimed to select the best vegetation indices for post-fire vegetation monitoring using remote sensing and classification methods for the temperate zone in southern Ecuador, as well as to analyze the vegetation’s development in different fire severity classes after a wildfire in September 2019. Random forest classification models were calculated using the fire severity classes (from the Relativized Burn Ratio—RBR) as a dependent variable and 23 multitemporal vegetation indices from 10 Sentinel-2 scenes as descriptive variables. The best vegetation indices to monitor post-fire vegetation recovery in the temperate Andes were found to be the Leaf Chlorophyll Content Index (LCCI) and the Normalized Difference Red-Edge and SWIR2 (NDRESWIR). In the first post-fire year, the vegetation had already recovered to a great extent due to vegetation types with a short life cycle (seasonal grass-species). Increasing index values correlated strongly with increasing fire severity class (fire severity class vs. median LCCI: 0.9997; fire severity class vs. median NDRESWIR: 0.9874). After one year, the vegetations’ vitality in low severity and moderate high severity appeared to be at pre-fire level.

Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content (Cab) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to Cab and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to Cab while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating Cab, while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables’ spectral response, leading to inconsistencies in the VI- Cab /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.

An exploration of solar-induced chlorophyll fluorescence (SIF) factors simulated by SCOPE for capturing GPP across vegetation types

Solar-induced chlorophyll fluorescence (SIF) has been regarded as proxy data of vegetation photosynthesis; thus, it is assimilated into the terrestrial carbon cycle modeling. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model is one of the most utilized models of SIF simulation. However, the currently incomplete understanding of SCOPE SIF factors and the lack of exploring how SCOPE works under different vegetation types would deteriorate further carbon cycle research. Herein, this study disentangled decisive SIF factors in the SCOPE model; then, a sample SIF dataset (SynSIF), with spatial resolutions of both 0.02∘ and 0.05∘, was simulated through SCOPE model using factors above. Then this study validated how far SCOPE simulating SIF could capture GPP, compared with other SIF datasets. The results showed that: (1) There are five decisive SIF factors in SCOPE model, including plant status (leaf chlorophyll content and leaf area index) and meteorological parameters (incoming shortwave radiation, air temperature, and atmospheric vapor pressure). (2) The linear relationship of SynSIF-GPP outachieved other SIF datasets across all six vegetation types in southern South America, Asia, and Africa, improving R2 averagely by 0.33, 0.28, and 0.15, respectively. (3) SynSIF in Oceania and Europe, revealing GPP better in shrublands (with SynSIF-GPP R2 increasing by 0.15 and 0.16, respectively) and grasslands (with SynSIF-GPP coefficients increasing by 0.14 and 0.06, respectively), illustrated spatially complementary characteristics with GOSIF across varying vegetation types. Thus, we anticipate that this study could provide more complete information for SCOPE simulating SIF in different biome research when estimating the terrestrial carbon cycle.

Arbuscular Mycorrhizal Fungi Inoculation Improves Flower Yield and Postharvest Quality Component of Gerbera Grown under Different Salinity Levels

High salinity levels of irrigated water and the accumulation of salt over time in the soil is a major concern worldwide, including in Jordan. The objective of this two-year study was to assess the influence of arbuscular mycorrhizal fungi (AMF) inoculation on the physiology, yield, and flower quality of gerbera (Gerbera jamesonii cvs. Beaudine and Palm Beach) under different salinity levels (0.0, 20.0 and 40.0 mM-NaCl). The study was arranged in a randomized complete block design with five replicates. During the experimental period (2018–2019), chlorophyll content index (SPAD), leaf gas exchange (photosynthesis, Pn; stomatal conductance, gs; transpiration, E), flower yield, flower quality (pedicel length and diameter, number of days to flowering, flower diameter, and vase life), root sporulation, and colonization were measured. Irrigation with saline water (20 and 40 mM-NaCl) significantly increased salt accumulation in soil. The mean soil electrical conductivity (EC) after two growing seasons for the 20 mM-NaCl treatment was 2.9 dS m−1 and 4.4 dS m−1 for the 40 mM. High salinity level (40 mM-NaCl) reduced root AMF sporulation by 53–62% and colonization by 12–25% across cultivars. Interestingly, root colonization was higher than 50% across salinity level and in both cultivars. Saline water at 40 mM-NaCl significantly reduced SPAD, Pn, gs, E, flower yield, and quality component, especially vase life. Interestingly, leaf chlorophyll content index from AMF-inoculated plants was significantly higher than uninoculated ones across cultivars at the second growing season. In addition, inoculation with AMF significantly increased yield in both ‘Beaudine’ (34–40%) and ‘Palm Beach’ (42–44%) cultivars and across the study period, 2018 to 2019. In addition, AMF increased vase life in ‘Beaudine’ by 19% to 28% and in ‘Palm Beach’ by 21% to 22%. Overall, our results revealed that gerbera growers can increase their flower yield and postharvest flower quality component (vase life) under saline conditions (soil EC &lt; 4.4 dS m−1) by inoculating the seedlings with AMF.

Estimating Leaf Chlorophyll Content of Moso Bamboo Based on Unmanned Aerial Vehicle Visible Images

Leaf chlorophyll content is an important indicator of the physiological and ecological functions of plants. Accurate estimation of leaf chlorophyll content is necessary to understand energy, carbon, and water exchange between plants and the atmosphere. The leaf chlorophyll content index (CCI) of 109 Moso bamboo samples (19 for training data, 19 for validation data, and 71 for extrapolation data) was measured from December 2019 to May 2021, while their corresponding red–green–blue (RGB) images were acquired using an unmanned aerial vehicle (UAV) platform. A method for estimating leaf CCI based on constructing relationships between field leaf CCI measurements and UAV RGB images was evaluated. The results showed that a modified excess blue minus excess red index and 1.4 × H-S in the hue–saturation–value (HSV) color space were the most suitable variables for estimating the leaf CCI of Moso bamboo. No noticeable difference in accuracy between the linear regression model and backpropagation neural network (BPNN) model was found. Both models performed well in estimating leaf CCI, with an R2 &gt; 0.85 and relative root mean square error (RMSEr) &lt; 15.0% for the validation data. Both models failed to accurately estimate leaf CCI during the leaf-changing period (April to May in off-year), with the problems being overestimation in low leaf CCI and underestimation in high leaf CCI values. At a flight height of 120 m and illumination between 369 and 546 W/m2, the CCI for an independent sample dataset was accurately estimated by the models, with an R2 of 0.83 and RMSEr of 13.78%. Flight height and solar intensity played a role in increasing the generality of the models. This study provides a feasible and straightforward method to estimate the leaf CCI of Moso bamboo based on UAV RGB images.