Soil Plant Analysis Development Values Research Articles

Temperature tolerance threshold and mechanism of oxidative damage in the leaf of Coffea arabica ‘Typica’ under heat stress

ABSTRACT Coffea arabica, an economically important crop, accounts for most of the coffee consumed globally. Increasing temperature due to climate change can cause a decrease in productivity in many crops, including coffee plants. The maximum temperature at which damage is induced has been reported for many crops, but it remains unclear in coffee plants. Here, we investigated the effect of different temperatures and the physiological damage induced by heat stress using both leaf disks and intact plants of Coffea arabica ‘Typica’. In the experiment using intact plants, we observed leaf damage by a decrease in soil plant analysis development value, and an increase in electrolyte leakage after exposure to 45°C for 96 h, whereas no leaf damage was observed for 72 h. The leaf surface temperatures after exposure to 45°C for 72 and 96 h were 44.0 and 46.3°C, respectively. Thus, a tolerance threshold in leaves of C. arabica ‘Typica’ under heat stress are likely between 44.0 and 46.3°C. The activities of catalase (CAT) and superoxide dismutase (SOD) decreased at 45°C in both leaf disks and intact plants. The decrease in the activities of SOD and CAT under heat stress may be responsible for the increased levels of reactive oxygen species, such as O2 − and H2O2, and the resulting cellular damage. Our findings provide valuable insights into the physiological responses of Coffea arabica ‘Typica’ to heat stress, which may contribute to the breeding and screening of tolerant cultivars in the future.

Flowering and Non-Flowering Spur Leaf Characteristics of 'Amasya' Apple and Its Comparison with Other Cultivars

Understanding the attributes of spur leaves in apple trees is essential to gain more insight into the complex process of fruit development and quality. However, limited literature is available about the spur leaf characteristics of the 'Amasya' apple cultivar. In this work, the Soil Plant Analysis Development (SPAD) and leaf area were investigated in flowering and non-flowering spur leaves of 'Amasya' and six common apple cultivars. Significant differences among cultivars were observed for the SPAD and spur leaf area. The median SPAD readings in 'Amasya' were 32.63 and 26.23 for the flowering and non-flowering spurs, respectively, which were the relatively low values among studied cultivars. The maximum SPAD value was measured in flowering spurs of 'Cripps Pink' (45.03). SPAD values were found to be lower in non-flowering spurs compared with flowering ones for all the studied cultivars, which confirms that decline in chlorophyll content coincided with a gradual decline in productivity. A notably significantly lower spur leaf area was found in 'Amasya', whereas the highest spur leaf area was in 'Cripps Pink'. However, no statistical difference was observed between flowering and non-flowering spur leaf areas within the same cultivars. The present results provided a general framework of SPAD and spur leaf areas in 'Amasya' and other apple cultivars when spurs had flowers or not. Knowledge of these characteristics provides a basis for building a model related to yield elaboration of apple trees.

Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt

Monitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spectral) as well as in situ spectra measurements can help decision takers to have deep insight on crop stress in a certain region. The research attempts to examine remote sensing dataset for forecasting wheat crop (Sakha 61) characteristics including the leaf area index (LAI), plant height (plant-h), above ground biomass (AGB) and Soil Plant Analysis Development (SPAD) value of wheat across non-stress, drought and salinity-induced stress in the Nile Delta region. In this context, the ability of in situ spectroradiometry measurements and QuickBird high resolution images was evaluated in our research. The efficiency of Random Forest (RF) and Artificial Neural Network (ANN), mathematical models was assessed to estimate the four measured wheat characteristics based on vegetation spectral reflectance indices (V-SRIs) extracted from both approaches and their interactions. Field surveys were carried out to collect in situ spectroradiometry measurements concomitant with the acquisition of QuickBird imagery. The results demonstrated that several V-SRIs extracted from in situ spectroradiometry data and the QuickBird image correlated with the LAI, plant-h, AGB, and SPAD value of wheat crop across the study site. The determination coefficient (R2) values of the association between V-SRIs of in situ spectroradiometry data and various determined wheat characteristics varied from 0.26 to 0.85. The ANN-GSIs-3 was found to be the optimum predictive model, demonstrating a greater relationship between the advanced features and LAI. The three features of V-SRIs comprised in this model were strongly significant for the prediction of LAI. The attained results indicated high R2 values of 0.94 and 0.86 for the training and validation phases. The ANN-GSIs-3 model constructed for the determination of chlorophyll in the plant which had higher performance expectations (R2 = 0.96 and 0.92 for training and validation datasets, respectively). In conclusion, the results of our study revealed that high resolution remote sensing images such as QuickBird or similar imagery, and in situ spectroradiometry measurements have the feasibility of providing necessary crop monitoring data across non-stressed and stressed (drought and salinity) conditions when integrating V-SRIs with ANN and RF algorithms.

The Effect of 8-Hydroxyquinoline Sulphate and Gibberellic Acid on Postharvest Viola odorata L. Leaf Longevity

The aim of the study was to evaluate the postharvest longevity of cut leaves of Viola odorata and determine their usefulness as a florist green. The research also involved the study of the influence of chemical compounds used as plant preservatives on the longevity of leaves of V. odorata. The leaf stalks of V. odorata were soaked in water or conditioned in a 200 mg·dm−3 solution of 8–hydroxyquinoline sulphate, or a 50 or 100 mg·dm−3 solution of gibberellic acid. After one day, the leaves were kept in water or 200 mg·dm−3 of 8–hydroxyquinoline sulphate. The leaves of V. odorata had a longevity of about 31 days in water. Both the conditioning in gibberellic acid and 8–hydroxyquinoline sulphate and the maintenance in 8–hydroxyquinoline sulphate reduced the vase life of cut V. odorata leaves. The application of conditioners resulted in a significantly greater reduction of the leaf mass. The Soil Plant Analysis Development value of the leaves was best affected by conditioning them in 100 mg·dm−3 gibberellic acid followed by keeping them in water. Cut leaves of V. odorata with water treatment may be used as a florist green.

Development of a high-accuracy autonomous sensing system for a field scouting robot

We developed a sensing system that can automatically acquire spectral reflection data using a hyperspectral camera with the aim of developing a field scouting robot. In order to deal with light-source fluctuations that occur during the daytime, we devised a sensing system equipped with artificial light that could be used at night. The fractional vegetation cover (FVC) and the soil plant analysis development (SPAD) value ​​were estimated by calculating the normalized difference vegetation index (NDVI) from the spectral reflection data, and the accuracy was verified by comparing the results with the results obtained during the daytime. When segmenting the vegetation using NDVI and calculating the FVC, the accuracy was 1.4% higher at night than in the day. There was a correlation between the FVC and the measured SPAD value with a coefficient of determination of 0.51 at night. For the regression of the NDVI and the SPAD value ​​of the manually extracted vegetation, the coefficient of determination was 0.85 at night and 0.77 in the day. As a result of sensing specific leaves every hour during the day, the difference in the SPAD value estimated from the NDVI was up to 7.9 SPAD units due to light fluctuation. In the regression of the NDVI and the SPAD value ​​of the vegetation automatically segmented by NDVI, the coefficient of determination did not change from 0.85 during the night, but decreased to 0.71 during the day because of the poor accuracy of the segmentation. The combination of NDVI wavelengths for identifying vegetation and the combination for estimating the SPAD value were almost the same during the night, but not during the day. Since the light environment is constant during nighttime sensing, vegetation information such as the FVC and the SPAD value can be estimated accurately. The method developed in this study, which does not require correction using a standard whiteboard, is suitable for autonomous sensing systems in field scouting robots because this method can minimize the need for human intervention during crop sensing.

Prediction of SPAD values for paddy leaves using smartphone-based direct contact imaging method

The Soil Plant Analysis Development (SPAD) value obtained from the SPAD meter is directly related to leaf chlorophyll content. The chlorophyll content is related to nitrogen content means the amount of fertilizer of a crop. Therefore, determining SPAD value is directly involved with crop health. Minolta SPAD meter can directly measure this value, and this is a well-established method in the research field for measuring chlorophyll content. Still, this instrument is too costly, which is beyond a farmers’ reach in the perspective of Bangladesh. The purpose of this study is to predict the SPAD value for paddy leaves using the smartphone-based direct contact imaging method to estimate the chlorophyll content of a paddy leaf. Numerous features were extracted from each image to predict the SPAD values. The features were then used as parameters in the multiple linear regression model. The models' performance was evaluated using images captured from a paddy field using a Minolta SPAD-502 Chlorophyll Meter. The multiple linear regression model's R2 and root mean square error (RMSE) values were 0.71 and 3.6512, respectively. Therefore, this result confirms that the direct digital contact imaging method has the potential to quantify the SPAD value of paddy leaves accurately. However, these results could be more accurate if the image acquisition was made from the seedling to mature stage of the paddy. In the future, an android app will be developed using this value which can directly measure the chlorophyll content of paddy leaves.

On-Site Detection of Nitrogen Content in Maize using Optical Sensor

Nutrients are essential for the growth of plants and they also increase the yield. Among the major nutrients, nitrogen is the most important nutrient for crop growth. The current study aims to estimate chlorophyll content vis-a-vis nitrogen content using different techniques and attempts to establish comparative benefits among available techniques. Maize crop being highly known for fertilizer responsiveness was chosen to carry out experiments with different levels of nitrogenous fertilizers. A preliminary field survey was taken in the farmer's field in Coimbatore and Salem districts. Two field experiments and one pot culture experiment at different doses of nitrogen input were conducted in 2019, 2020, and 2021 in the Department of Nano science and Technology at Tamil Nadu Agricultural University. In this study conventional nitrogen was applied with various dosage viz.,0 kgha-1(T1), 50 kgha-1 (T2), 100 kgha-1 (T3), 150 kgha-1 (T4), 200 kgha-1 (T5), 250 kgha-1 (T6), 300 kgha-1 (T7). In the pot culture experiment, IFFCO Nano nitrogen was sprayed on the maize leaves with seven different doses which were control (no spray) (T1), conventional nitrogen (T2), 0.5X RDF(Recommended Dose of Fertilizer) (T3), 1.0X RDF (T4), 1.5X RDF (T5), 2.0X RDF (T6), 2.5X RDF (T7). The chlorophyll content in all the trials were estimated using Dimethyl sulfoxide(DMSO), a portable in-house developed nutrient meter, and Spectroradio meter method. Chlorophyll content was measured at two different growth stages of maize viz., 60 & 90 days after sowing. The total nitrogen was analysed using Kjeldahl method in the maize leaf. Among the treatments, with different doses of nitrogen levels, 300 kgha-1 RDN & 2.5X RDF (T7) recorded higher SPAD (Soil Plant Analysis Development) values, chlorophyll “a” and chlorophyll “b” content and total nitrogen content at 60 DAS (Days After Sowing) in all tested methods. Chlorophyll and total nitrogen content were significantly decreased in later days. In a stressed environment with no nitrogen (T1) applied condition the chlorophyll and nitrogen content showed significantly lower levels

Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value.

Chlorophyll content is an important indicator of plant photosynthesis, which directly affects the growth and yield of crops. Using hyperspectral imaging technology to quickly and non-destructively estimate the soil plant analysis development (SPAD) value of pepper leaf and its distribution inversion is of great significance for agricultural monitoring and precise fertilization during pepper growth. In this study, 150 samples of pepper leaves with different leaf positions were selected, and the hyperspectral image data and SPAD value were collected for the sampled leaves. The correlation coefficient, stability competitive adaptive reweighted sampling (sCARS), and iteratively retaining informative variables (IRIV) methods were used to screen characteristic bands. These were combined with partial least-squares regression (PLSR), extreme gradient boosting (XGBoost), random forest regression (RFR), and gradient boosting decision tree (GBDT) to build regression models. The developed model was then used to build the inversion map of pepper leaf chlorophyll distribution. The research results show that: (1) The IRIV-XGBoost model demonstrates the most comprehensive performance in the modeling and inversion stages, and its , , and are 0.81, 2.76, and 2.30, respectively; (2) The IRIV-XGBoost model was used to calculate the SPAD value of each pixel of pepper leaves, and to subsequently invert the chlorophyll distribution map of pepper leaves at different leaf positions, which can provide support for the intuitive monitoring of crop growth and lay the foundation for the development of hyperspectral field dynamic monitoring sensors.

Rice Height Monitoring between Different Estimation Models Using UAV Photogrammetry and Multispectral Technology

Unmanned aerial vehicle (UAV) photogrammetry was used to monitor crop height in a flooded paddy field. Three multi-rotor UAVs were utilized to conduct flight missions in order to capture RGB (RedGreenBlue) and multispectral images, and these images were analyzed using several different models to provide the best results. Two image sets taken by two UAVs, mounted with RGB cameras of the same resolution and Global Navigation Satellite System (GNSS) receivers of different accuracies, were applied to perform photogrammetry. Two methods were then proposed for creating crop height models (CHMs), one of which was denoted as the M1 method and was based on the Digital Surface Point Cloud (DSPC) and the Digital Terrain Point Cloud (DSPT). The other was denoted as the M2 method and was based on the DSPC and a bathymetric sensor. An image set taken by another UAV mounted with a multispectral camera was used for multispectral-based photogrammetry. A Normal Differential Vegetation Index (NDVI) and a Vegetation Fraction (VF) were then extracted. A new method based on multiple linear regression (MLR) combining the NDVI, the VF, and a Soil Plant Analysis Development (SPAD) value for estimating the measured height (MH) of rice was then proposed and denoted as the M3 method. The results show that the M1 method, the UAV with a GNSS receiver with a higher accuracy, obtained more reliable estimations, while the M2 method, the UAV with a GNSS receiver of moderate accuracy, was actually slightly better. The effect on the performance of CHMs created by the M1 and M2 methods is more negligible in different plots with different treatments; however, remarkably, the more uniform the distribution of vegetation over the water surface, the better the performance. The M3 method, which was created using only a SPAD value and a canopy NDVI value, showed the highest coefficient of determination (R2) for overall MH estimation, 0.838, compared with other combinations.

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

Genetic Improvement of Post-Heading Root Morphology and Physiology Facilitating Yield Increase of japonica Inbred Rice

Since genetic improvement greatly promoted an increased yield japonica inbred rice in east China after the 1990s, better root characteristics were certainly expected. In 2018 and 2019, nine japonica inbred rice released in the 1990s, 2000s, and 2010s were investigated to evaluate the changes in root morpho-physiology and identify root traits that contributed to the positive yield trends during the genetic process. The 2010’s rice had 8.0 and 4.3% higher grain yield than the 1990’s and the 2000’s rice, respectively (p < 0.05). Genetic yield gain was mainly attributed to the increased spikelets per panicle. Compared with the 1990’s and the 2000’s rice, the 2010’s rice had higher shoot biomass at heading and maturity (p < 0.05), as well as root biomass (p < 0.05), especially for root biomass of 15–30 cm soil depth. Leaf area index (LAI), soil-plant analysis development (SPAD) values, and leaf photosynthetic rate at middle grain-filling period (MGP) and late grain-filling period (LGP) were all increased. The 2010’s rice had consistently higher root length and volume, root oxidation activity, and root bleeding rate at MGP and LGP than the 1990’s and the 2000’s rice (p < 0.05). Positive correlations were detected between root length and volume, root oxidation activity, and root bleeding rate at MGP, LGP, and SPAD values, leaf photosynthetic rate at MGP and LGP, and higher shoot biomass accumulation after heading and grain yield (p < 0.05 or p < 0.01). The present study implied that genetic improvement optimized post-heading root morphology and physiology, which maintained shoot stay-green and facilitated biomass accumulation and yield increase in japonica inbred rice during the genetic process since the 1990s.

Effect of different seedling growing methods on the SPAD, NDVI values and some morphological parameters of four sweet corn (Zea mays L.) hybrids

Abstract The main goal of our investigation was to determine the relationship between different growing methods of sweet corn seedlings and some physiological and morphological parameters of four hybrids in order to get information about the ability of their stress tolerance in a two-year experiment (2019, 2020). Seedlings were grown with and without pre-conditioning. Pre-conditioning is based on growing young plants exposing them to cold stress. Seedling emergence percentage, plant height, total leaf number, the total mass of fresh aboveground biomass, and ear length were determined as well as Soil Plant Analysis Development (SPAD) and Normalized Difference Vegetation Index (NDVI) values. In 2019, the pre-conditioned seedlings were more tolerant to cold stress for most of the tested parameters. Overall, the SPAD and NDVI values of the pre-conditioned seedlings were considered better in both years, however, it was not verified for all the studied hybrids. Among the hybrids, ‘Strongstar’ had the highest benefit from pre-conditioning compared to the standard growing method in terms of resulting in 17.5% higher plants, 13.1% longer ears, and 10.4% higher SPAD values in 2019. In 2020, when the cold stress was not so dominant, ‘Gyöngyhajnal’ gained the most from pre-conditioning with 9.7% higher plants, 32% more fresh aboveground biomass, 6.8% longer ears, 3.6% higher SPAD, and 9.3% higher NDVI values. More emphasis should be placed on the choice of stress-tolerant hybrids as well as on the seedling growing method and the date of transplanting to improve adaptation to the more frequent weather extremes.

Winter wheat SPAD estimation from UAV hyperspectral data using cluster-regression methods

Soil plant analysis development (SPAD) values indicate the relative chlorophyll content in leaves. Chlorophyll plays a vital role in wheat growth and fertilization management as a photosynthetic agent. Unmanned aerial vehicle (UAV) hyperspectral data, combined with measured SPAD values in fields, are widely used to study wheat chlorophyll concentrations over time. In this study, considering the spectral differences in fields with different soil fertility, we propose a new modeling method named as cluster-regression to estimate the winter wheat SPAD at a national soil observation and experimental station in northern Anhui, China. The wheat spectrum was divided into several clusters according to the spectral angular distance, and regression models were built between wheat spectrum and the measured SPAD for each cluster. We used K-means as the clustering method and two types of ensemble learning algorithms, namely, random forests and Extreme Gradient Boosting (XGBoost) as the regression methods. The root-mean-square error (RMSE), median absolute percentage error (MAPE), and R2 were used as model accuracy indicators. The results showed that the XGBoost model slightly outperformed the random forest in wheat SPAD estimation. Coupled with the first step of K-means clustering, the overall performance of the regression models can be easily improved, and cluster-XGBoost showed the best performance, with an RMSE of 1.444, MAPE of 2.36%, and R2 of 0.925. Moreover, addition of soil organic matter and soil total nitrogen positively impacted the accuracy of SPAD estimation models. This study provides an effective UAV hyperspectral technique for wheat SPAD concentration estimation and demonstrates the impact of soil organic matter and total nitrogen on wheat SPAD estimation.

Development of a low-cost portable device for pixel-wise leaf SPAD estimation and blade-level SPAD distribution visualization using color sensing

Soil Plant Analysis Development (SPAD) was proved as a reliable indicator for the representation of leaf chlorophyll or nitrogen content. Portable plant SPAD sensors have been widely studied to determine plant nitrogen status rapidly in recent years. However, the SPAD-502 device could only measure a single point in one shot, and its price was high. Moreover, most of the presented DIY devices for SPAD evaluation required clamping operation to get the transmitted light signal using complex hardware architecture. In this study, an ultra-portable SPAD evaluation system, defined as SPAD-Cap, was proposed for leaf SPAD distribution analysis. The RGB camera module, Raspberry Pi Zero, and light source were integrated. Two modeling methods, including partial least square regression (PLSR) and convolutional neural network for regression (CNN-R), were used to evaluate pixel-level SPAD value based on the color features of the corresponding pixel. The SPAD distribution map was generated by traversing all pixels of the tested blade sample using the trained model. A Web GUI was developed for controlling the whole system and accessing the visualization result. The testing result showed that the designed SPAD-Cap achieved R2 of 0.97 and RMSE of 2.5 SPAD units to inspect rape leaf SPAD. For leaves in several species, R2 was 0.91, and RMSE was about 4.0 SPAD. Moreover, all the processing steps, including data collection, processing, storage, and visualization, were involved in the SPAD-Cap system, which could be a potential solution for rapid and automatic inspection of leaf SPAD distribution.

Performance of Hybrid Wheat Cultivars Facing Deficit Irrigation under Semi-Arid Climate in Pakistan

Predicted decrease in water availability for crop production and uncertainty in climatic conditions require devising the irrigation strategies to increase water use efficiency (WUE) for sustainable crop production. The development of crop cultivars with higher WUE is a pre-requisite for such strategies, particularly in developing countries, including Pakistan, who face stern food security challenges. A two-year field study was conducted following a split-plot randomized complete block design to understand the effects of wheat cultivars (hybrid cultivars, 18A-1 and 18A-2, and local cultivar Ghaneemat IBGE-2016), sowing dates (15th November, 30th November, and 15th December), and irrigation regimes [I (103 mm), II (175 mm), III (254 mm), and IV (330 mm)] at four different growth stages of tillering, booting, anthesis and grain filling on wheat productivity, biomass production and grain yield, and crop-water relations. Early sown hybrid cultivars 18A-1 and 18A-2 showed significantly higher biological and grain yields compared to the local cultivar (59% and 69% higher than the local cultivar). Trends in biomass production and grain yield were also similar at later sowing dates of 30th November and 15th December. However, biological and grain yields decreased with delay in sowing for each cultivar. The data also revealed that hybrid cultivars were better suited to deficient irrigation and generally produced significantly higher biological and grain yields under each moisture regime. Cultivars, sowing dates, and irrigation regime differed significantly for their effects on the Soil Plant Analysis Development (SPAD) values, chlorophyll a and b contents but not for carotenoids. Sowing dates and irrigation regimes had significant effects on relative water content (RWC), water saturation deficit (WSD), water uptake capacity (WUC), and water retention capacity (WRC); however, only WUC varied significantly between the cultivars. The phenological data show that hybrid cultivars took more days to maturity and grain filling than the local cultivar, and days decreased with delayed sowing. The biological and grain yields show significant positive correlations with SPAD values (p < 0.001) and days to maturity (p < 0.001). Our study shows that hybrid wheat cultivars can be opted for higher biomass production and grain yields under deficit irrigation scenarios of semi-arid climatic conditions in Pakistan. Moreover, the hybrid wheat cultivars can perform better than the indigenous cultivar even for delayed sowing dates of 30th November and 15th December.

Association of leaf chlorophyll content with the stay-green trait and grain yield in wheat grown under heat stress conditions

Heat stress is a major production constraint of wheat in South Asia, particularly in the Gangetic plains of India and Bangladesh. The leaf chlorophyll status is a key determinant for a high rate of photosynthesis under stress. The present experiments included 238 genotypes in 2016–2017 and 321 genotypes in 2017–2018 under optimum and under heat stress conditions. Subsequently, a set of 100 genotypes selected on basis of the heat susceptibility index was evaluated in 2018–2019 under heat stress conditions to study the relationship between important physiological traits and yield under stress. A significant correlation of soil plant analysis development (SPAD) value of the two upper leaves with stay-green trait and grain yield indicates the importance of chlorophyll content, both in flag and penultimate leaf, in maintaining leaf areas under greenness (LAUG) and grain yield under heat stress. The SPAD in the flag and penultimate leaf was responsible for 8.8% and 10.9%, respectively, of the variation in grain yield. For the stay-green trait, 8.4% and 7.2 % of the variation was governed by the SPAD value in the flag and penultimate leaf, respectively. These results suggest that, in addition to the flag leaf, the chlorophyll status of the penultimate leaf can be an important criterion for the selection of superior wheat genotypes under heat stress. The genotypes SW-139; SW 108; DWR-F8-35-9-1; NHP-F8-130; DWR-F8-3-1 that maintained a high chlorophyll content in the flag and penultimate leaf can be used further in breeding programmes addressing heat resistance in wheat.

Estimation of nitrogen status and yield of rice crop using unmanned aerial vehicle equipped with multispectral camera

Nitrogen is one of the essential nutrients required for crop growth, and hence should be applied efficiently for attaining optimum yield. To fulfil nitrogen need, absorbed nitrogen in the plant is required to be estimated. Various methods are available to estimate crop nitrogen such as tissue analysis using the methods of Kjeldahl and Dumas, which are accurate, but time-consuming and destructive. Satellite imagery provides a more extensive field view. However, they are limited to their spatial and temporal resolution. Unmanned aerial vehicle (UAV) is emerging as a promising tool that can provide the status of crop nitrogen rapidly with high spatial and temporal resolution. The objective of the study was to evaluate UAV-based imageries to show nitrogen status and predict rice yield at different growth stages. The experiments were conducted using two rice cultivars, six nitrogen applications, two water management practices, and with three replications. Soil plant analysis development (SPAD) meter readings were collected at various growth stages. First, aerial imageries of experimental site were collected using an octocopter UAV equipped with a multispectral sensor that provides reflectance values in four different bands (red, green, red edge, and near-infrared) along with SPAD values for respective flight. Second, aerial images were processed in pix4D software, to identify the most appropriate vegetation index that shows nitrogen status variation in the field and to predict yield using different vegetation indices. Nine vegetation indices were considered: ratio vegetation index, normalized difference vegetation index, normalized green red difference index, red edge difference vegetation index, green ratio vegetation index, green normalized difference vegetation index (GNDVI), wide dynamic range vegetation index, transformed normalized difference vegetation index (TNDVI), and normalized difference red edge. After that, a linear regression model was developed between the representative index and SPAD values. Finally, linear regression models developed by using VI and SPAD values were evaluated and results revealed that GNDVI-based model simulates SPAD values with R2 of 0.49, 0.49, and 0.74 at panicle, milky, and booting stages, respectively. It is also found that TNDVI-based linear regression model predicts yield with R2 of 0.71 at milky stage.

Assessment of Nutrient Status of Lowland Irrigated Rice (Oryza sativa L.) using Low Altitude Remote Sensing

Low altitude remote sensing provides near real-time information on crop nutrient status with higher spatial resolution compared to airborne and spaceborne systems. In this study, an Unmanned Aerial Vehicle System (UAVS) with on-board multispectral camera was deployed to determine the normalized difference vegetation index (NDVI) of lowland irrigated rice under different fertilizer treatments. Relationship between UAV-based NDVI and ground measurements from Soil Plant Analysis Development (SPAD) chlorophyll meter at different rice growth stages were also established. The results showed that experimental plots with high nitrogen fertilization had significantly higher SPAD and NDVI values compared with plots without nitrogen fertilization. Strong correlation between SPAD readings and NDVI values (p<0.001) was also observed as early as panicle initiation stage (r=0.7372). Hence, low altitude remote sensing can be a rapid and non-destructive tool for site-specific rice nutrient management. The use of low altitude remote sensing technique as evaluated in this study offers a practical approach in spot- or field-level application of nitrogen fertilizer during panicle initiation to enhance uniform spikelet development, and during flowering (few days before or after) to promote grain filling using foliar fertilizers.

Feasibility Study of Using Absolute SPAD Values for Standardized Evaluation of Corn Nitrogen Status

Nitrogen fertilizer recommendations for corn (Zea mays L.) should ensure high yields using adequate N doses. Soil–plant analysis development (SPAD) meter technology using absolute SPAD values, might be more reliable than relative SPAD values in helping corn producers making timely decisions about N applications. This study aimed to develop a relationship between absolute SPAD values and leaf N concentration, and to determine optimal leaf N concentrations at early corn growth stages (V6, V8, V10, and V12). Three experiments were conducted during two summer seasons (2014 and 2015) using six N treatments applied at early corn growth stages. In parallel, two experiments were carried out in a high residual N environment to establish the optimum corn leaf N concentration. Results showed a significant linear relationship between corn leaf N concentrations and absolute SPAD values (R2 = 0.80, p < 0.05). The mean optimum corn leaf N concentration decreased over corn growth stages. It is of great importance to make the absolute SPAD method accessible for farmers, but more research is required to perform standardized reading of absolute SPAD values data.

A Preliminary Study on the Response of Confectionery Sunflower (Helianthus annuus L.) to Arbuscular Mycorrhizal Fungi (AMF)

This preliminary study was carried out to investigate the effect of arbuscular mycorrhizal fungi (AMF) on yield, seed characters and chlorophyll content of confectionery sunflower (Helianthus annuus L.). The experiment was conducted at Tavas/Denizli in the 2018 growing season. The experimental design was “Randomized Complete Block Design (RCBD)” with four replications. Seed coating and soil spraying of AMF applications were compared with control (non-treatment AMF). The differences among treatments were significant for plant height (cm), head diameter (cm), seed width (mm), single plant yield (g), thousand kernel weight (g), yield (kg ha-1), chlorophyll content index (CCI) and soil plant analysis development (SPAD) values. The seed coating had significant and higher values for plant height (cm), head diameter (cm), seed width (mm), single plant yield (g), thousand kernel weight (g), yield (kg ha-1), chlorophyll content index (CCI) and SPAD values, whereas hull ratio was found to be similar between AMF applications and control. It was concluded that seed coating with AMF can be profitable for confectionery sunflower producers due to increased yield and low cost. The results of this preliminary study should be supported by findings from multiple locations and years.