Water Stress Alters Morphophysiological, Grain Quality and Vegetation Indices of Soybean Cultivars

Morpho-Physiological Traits and Oil Quality in Drought-Tolerant Raphanus sativus L. Used for Biofuel Production

Raphanus sativus L. is a potential source of raw material for biodiesel fuel due to the high oil content in its grains. In Brazil, this species is cultivated in the low rainfall off-season, which limits the productivity of the crop. The present study investigated the effects of water restriction on the physiological and biochemical responses, production components, and oil quality of R. sativus at different development stages. The treatments consisted of 100% water replacement (control), 66%, and 33% of field capacity during the phenological stages of vegetative growth, flowering, and grain filling. We evaluated characteristics of water relations, gas exchange, chlorophyll a fluorescence, chloroplast pigment, proline, and sugar content. The production components and chemical properties of the oil were also determined at the end of the harvest cycle. Drought tolerance of R. sativus was found to be mediated primarily during the vegetative growth stage by changes in photosynthetic metabolism, stability of photochemical efficiency, increased proline concentrations, and maintenance of tissue hydration. Grain filling was most sensitive to water limitation and showed a reduction in yield and oil content. However, the chemical composition of the oil was not altered by the water deficit. Our data suggest that R. sativus is a drought-tolerant species.

Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System

Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand's unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (R2=0.78 and RMSE = 2.4 µg∙cm-2), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions.

Sensor-based precision nutrient and irrigation management enhances the physiological performance, water productivity, and yield of soybean under system of crop intensification

Sensor-based decision tools provide a quick assessment of nutritional and physiological health status of crop, thereby enhancing the crop productivity. Therefore, a 2-year field study was undertaken with precision nutrient and irrigation management under system of crop intensification (SCI) to understand the applicability of sensor-based decision tools in improving the physiological performance, water productivity, and seed yield of soybean crop. The experiment consisted of three irrigation regimes [I1: standard flood irrigation at 50% depletion of available soil moisture (DASM) (FI), I2: sprinkler irrigation at 80% ETC (crop evapo-transpiration) (Spr 80% ETC), and I3: sprinkler irrigation at 60% ETC (Spr 60% ETC)] assigned in main plots, with five precision nutrient management (PNM) practices{PNM1-[SCI protocol], PNM2-[RDF, recommended dose of fertilizer: basal dose incorporated (50% N, full dose of P and K)], PNM3-[RDF: basal dose point placement (BDP) (50% N, full dose of P and K)], PNM4-[75% RDF: BDP (50% N, full dose of P and K)] and PNM5-[50% RDF: BDP (50% N, full P and K)]} assigned in sub-plots using a split-plot design with three replications. The remaining 50% N was top-dressed through SPAD assistance for all the PNM practices. Results showed that the adoption of Spr 80% ETC resulted in an increment of 25.6%, 17.6%, 35.4%, and 17.5% in net-photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), and intercellular CO2 concentration (Ci), respectively, over FI. Among PNM plots, adoption of PNM3 resulted in a significant (p=0.05) improvement in photosynthetic characters like Pn (15.69 µ mol CO2 m-2 s-1), Tr (7.03 m mol H2O m-2 s-1), Gs (0.175 µmol CO2 mol-1 year-1), and Ci (271.7 mol H2O m2 s-1). Enhancement in SPAD (27% and 30%) and normalized difference vegetation index (NDVI) (42% and 52%) values were observed with nitrogen (N) top dressing through SPAD-guided nutrient management, helped enhance crop growth indices, coupled with better dry matter partitioning and interception of sunlight. Canopy temperature depression (CTD) in soybean reduced by 3.09-4.66°C due to adoption of sprinkler irrigation. Likewise, Spr 60% ETc recorded highest irrigation water productivity (1.08 kg ha-1 m-3). However, economic water productivity (27.5 INR ha-1 m-3) and water-use efficiency (7.6 kg ha-1 mm-1 day-1) of soybean got enhanced under Spr 80% ETc over conventional cultivation. Multiple correlation and PCA showed a positive correlation between physiological, growth, and yield parameters of soybean. Concurrently, the adoption of Spr 80% ETC with PNM3 recorded significantly higher grain yield (2.63 t ha-1) and biological yield (8.37 t ha-1) over other combinations. Thus, the performance of SCI protocols under sprinkler irrigation was found to be superior over conventional practices. Hence, integrating SCI with sensor-based precision nutrient and irrigation management could be a viable option for enhancing the crop productivity and enhance the resource-use efficiency in soybean under similar agro-ecological regions.

Water Stress Alters Physiological, Spectral, and Agronomic Indexes of Wheat Genotypes

Selecting drought-tolerant and more water-efficient wheat genotypes is a research priority, specifically in regions with irregular rainfall or areas where climate change is expected to result in reduced water availability. The objective of this work was to use high-throughput measurements with morphophysiological traits to characterize wheat genotypes in relation to water stress. Field experiments were conducted from May to September 2018 and 2019, using a sprinkler bar irrigation system to control water availability to eighteen wheat genotypes: BRS 254; BRS 264; CPAC 01019; CPAC 01047; CPAC 07258; CPAC 08318; CPAC 9110; BRS 394 (irrigated biotypes), and Aliança; BR 18_Terena; BRS 404; MGS Brilhante; PF 020037; PF 020062; PF 120337; PF 100368; PF 080492; and TBIO Sintonia (rainfed biotypes). The water regimes varied from 22 to 100% of the crop evapotranspiration replacement. Water stress negatively affected gas exchange, vegetation indices, and grain yield. High throughput variables TCARI, NDVI, OSAVI, SAVI, PRI, NDRE, and GNDVI had higher yield and morphophysiological measurement correlations. The drought resistance index indicated that genotypes Aliança, BRS 254, BRS 404, CPAC 01019, PF 020062, and PF 080492 were more drought tolerant.

Water Stress and Seed Color Interacting to Impact Seed and Oil Yield, Protein, Mucilage, and Secoisolariciresinol Diglucoside Content in Cultivated Flax (Linum usitatissimum L.)

Flaxseed (Linum usitatissimum L.) is a plant with a wide range of medicinal, health, nutritional, and industrial uses. This study assessed the genetic potential of yellow and brown seeds in thirty F4 families under different water conditions concerning seed yield, oil, protein, fiber, mucilage, and lignans content. Water stress negatively affected seed and oil yield, while it positively affected mucilage, protein, lignans, and fiber content. The total mean comparison showed that under normal moisture conditions, seed yield (209.87 g/m²) and most quality traits, including oil (30.97%), secoisolariciresinol diglucoside (13.89 mg/g), amino acids such as arginine (1.17%) and histidine (1.95%), and mucilage (9.57 g/100 g) were higher in yellow-seeded genotypes than the brown ones ((188.78 g/m²), (30.10%), (11.66 mg/g), (0.62%), (1.87%), and (9.35 g/100 g), respectively). Under water stress conditions, brown-seeded genotypes had a higher amount of fiber (16.74%), seed yield (140.04 g/m²), protein (239.02 mg. g^-1), methionine (5.04%), and secondary metabolites such as secoisolariciresinol diglucoside (17.09 mg/g), while their amounts in families with yellow seeds were 14.79%, 117.33 g/m², 217.12 mg. g^-1, 4.34%, and 13.98 mg/g, respectively. Based on the intended food goals, different seed color genotypes may be appropriate for cultivation under different moisture environments.

Dynamic Regulation of Grapevine's microRNAs in Response to Mycorrhizal Symbiosis and High Temperature

MicroRNAs (miRNAs) are non-coding small RNAs that play crucial roles in plant development and stress responses and can regulate plant interactions with beneficial soil microorganisms such as arbuscular mycorrhizal fungi (AMF). To determine if root inoculation with distinct AMF species affected miRNA expression in grapevines subjected to high temperatures, RNA-seq was conducted in leaves of grapevines inoculated with either Rhizoglomus irregulare or Funneliformis mosseae and exposed to a high-temperature treatment (HTT) of 40 °C for 4 h per day for one week. Our results showed that mycorrhizal inoculation resulted in a better plant physiological response to HTT. Amongst the 195 identified miRNAs, 83 were considered isomiRs, suggesting that isomiRs can be biologically functional in plants. The number of differentially expressed miRNAs between temperatures was higher in mycorrhizal (28) than in non-inoculated plants (17). Several miR396 family members, which target homeobox-leucine zipper proteins, were only upregulated by HTT in mycorrhizal plants. Predicted targets of HTT-induced miRNAs in mycorrhizal plants queried to STRING DB formed networks for Cox complex, and growth and stress-related transcription factors such as SQUAMOSA promoter-binding-like-proteins, homeobox-leucine zipper proteins and auxin receptors. A further cluster related to DNA polymerase was found in R. irregulare inoculated plants. The results presented herein provide new insights into miRNA regulation in mycorrhizal grapevines under heat stress and can be the basis for functional studies of plant-AMF-stress interactions.

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

INTRODUCTION

Canopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative.

METHODS

To solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables.

RESULTS

The analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R² = 0.91076, RMSE = 0.00070; validation set; R² = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R² of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%.

DISCUSSION

This study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.