Soil Plant Analysis Development Values Research Articles

Genome-wide association studies of salinity tolerance in local aman rice.

The present study aimed to identify and characterize new sources of salt tolerance among 94 rice varieties from varied geographic origins. The genotypes were divided into five groups based on their morphological characteristics at both vegetative and reproductive stages using salinity scores from the Standard Evaluation System (SES). The experiment was designed as per CRD (Completely Randomized Design) with 2 sets of salinity treatments for 8 dS/meter and 12 dS/meter, respectively compared with one non-salinized control set. Using a Soil Plant Analysis Development (SPAD) meter, assessments of the apparent chlorophyll content (greenness) of the genotypes were done to comprehend the mechanism underlying their salt tolerance. To evaluate molecular genetic diversity, a panel of 1 K RiCA SNP markers was employed. Utilizing TASSEL 5.0 software, 598 filtered SNPs were used for molecular analysis. Whole-genome association studies (GWAS) were also used to investigate panicle number per plant (pn, tiller number per plant (till), SPAD value (spad), sterility (percent) (str), plant height (ph) and panicle length (pl. It is noteworthy that these characteristics oversee conveying the visible signs of salt damage in rice. Based on genotype data, diversity analysis divided the germplasm groups into four distinct clusters (I, II, III and IV). For the traits studied, thirteen significant marker-trait associations were discovered. According to the phenotypic screening, seven germplasm genotypes namely Koijuri, Asha, Kajal, Kaliboro, Hanumanjata, Akundi and Dular, are highly tolerant to salinity stress. The greenness of these genotypes was found to be more stable over time, indicating that these genotypes are more resistant to stress. Regarding their tolerance levels, the GWAS analysis produced comparable results, supporting that salinity-tolerant genotypes having minor alleles in significant SNP positions showed more greenness during the stress period. The Manhattan plot demonstrated that at the designated significant SNP position, the highly tolerant genotypes shared common alleles. These genotypes could therefore be seen as important genomic resources for accelerating the development and release of rice varieties that are tolerant to salinity.

Identification of quantitative trait loci (QTLs) regulating leaf SPAD value and trichome density in mungbean (Vigna radiata L.) using genotyping-by-sequencing (GBS) approach.

Quantitative trait loci (QTL) mapping is used for the precise localization of genomic regions regulating various traits in plants. Two major QTLs regulating Soil Plant Analysis Development (SPAD) value (qSPAD-7-1) and trichome density (qTric-7-2) in mungbean were identified using recombinant inbred line (RIL) populations (PMR-1×Pusa Baisakhi) on chromosome 7. Functional analysis of QTL region identified 35 candidate genes for SPAD value (16 No) and trichome (19 No) traits. The candidate genes regulating trichome density on the dorsal leaf surface of the mungbean include VRADI07G24840, VRADI07G17780, and VRADI07G15650, which encodes for ZFP6, TFs bHLH DNA-binding superfamily protein, and MYB102, respectively. Also, candidate genes having vital roles in chlorophyll biosynthesis are VRADIO7G29860, VRADIO7G29450, and VRADIO7G28520, which encodes for s-adenosyl-L-methionine, FTSHI1 protein, and CRS2-associated factor, respectively. The findings unfolded the opportunity for the development of customized genotypes having high SPAD value and high trichome density having a possible role in yield and mungbean yellow vein mosaic India virus (MYMIV) resistance in mungbean.

Genome-wide association studies for earliness, MYMIV resistance, and other associated traits in mungbean (Vigna radiata L. Wilczek) using genotyping by sequencing approach.

Yellow mosaic disease (YMD) remains a major constraint in mungbean (Vigna radiata (L.)) production; while short-duration genotypes offer multiple crop cycles per year and help in escaping terminal heat stress, especially during summer cultivation. A comprehensive genotyping by sequencing (GBS)-based genome-wide association studies (GWAS) analysis was conducted using 132 diverse mungbean genotypes for traits like flowering time, YMD resistance, soil plant analysis development (SPAD) value, trichome density, and leaf area. The frequency distribution revealed a wide range of values for all the traits. GBS studies identified 31,953 high-quality single nucleotide polymorphism (SNPs) across all 11 mungbean chromosomes and were used for GWAS. Structure analysis revealed the presence of two genetically distinct populations based on ΔK. The linkage disequilibrium (LD) varied throughout the chromosomes and at r2 = 0.2, the mean LD decay was estimated as 39.59 kb. Two statistical models, mixed linear model (MLM) and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) identified 44 shared SNPs linked with various candidate genes. Notable candidate genes identified include FPA for flowering time (VRADI10G01470; chr. 10), TIR-NBS-LRR for mungbean yellow mosaic India virus (MYMIV) resistance (VRADI09G06940; chr. 9), E3 ubiquitin-protein ligase RIE1 for SPAD value (VRADI07G28100; chr. 11), WRKY family transcription factor for leaf area (VRADI03G06560; chr. 3), and LOB domain-containing protein 21 for trichomes (VRADI06G04290; chr. 6). In-silico validation of candidate genes was done through digital gene expression analysis using Arabidopsis orthologous (compared with Vigna radiata genome). The findings provided valuable insight for marker-assisted breeding aiming for the development of YMD-resistant and early-maturing mungbean varieties.

Stratified diagnosis of cotton canopy spectral characteristics based on CWT-SPA and its relationship with moisture, nitrogen, and SPAD values

ABSTRACT Cotton is an economically valuable crop worldwide, and accurate and rapid leaf moisture content, nitrogen content, and soil plant analysis development (SPAD) value estimations are crucial for cotton growth. In this study, high-resolution spectral data were collected for the Tahe 2 cotton variety in Alar City, Xinjiang. The raw spectra were preprocessed using four methods: first derivative, standard normal variate transformation, second derivative, and multiplicative scatter correction. Wavelet coefficients at multiple scales were generated from the raw spectra using a continuous wavelet transform (CWT) and CWT with first derivative (CWT-FD). A quantitative detection model combining CWT and CWT-FD with successive projection algorithm – partial least squares (SPA-PLS) was developed to estimate the leaf moisture content, nitrogen content, and SPAD values. The performances of the different scales and methods for estimating these parameters were compared. Six stratification methods were employed to determine the optimal modelling results and decomposition scales for the parameters under various stratification schemes. The results demonstrated that the CWT and CWT-FD methods significantly improved the accuracy of estimating cotton canopy spectral characteristics related to the parameters, surpassing that of conventional spectral processing methods. The mexh, db5, and db5 wavelet basis functions, along with scale 3, were identified as the optimal modelling choices for moisture content, nitrogen content, and SPAD values throughout the cotton plant growth period. Among the six vertical stratification structures of cotton, the CWT and CWT-FD methods, along with the three wavelet basis functions, achieved the best results for the three parameters in the upper layer. The optimal relative percent deviation values for the three parameters of typical leaves ranged from 1.603 to 2.016, indicating satisfactory modelling performance. These findings provide a theoretical foundation for accurately estimating leaf moisture content, nitrogen content, and SPAD values using spectral technology, enabling the simultaneous extraction and monitoring of multiple physiological indicators.

Precision agricultural technology for advanced monitoring of maize yield under different fertilization and irrigation regimes: A case study in Eastern Hungary (Debrecen)

Precision agricultural (PrA) technology relies on the utilization of special equipment to access real time observations on plant health status, chlorophyll, nitrogen content, and soil moisture content. In this research new PrA technology (i.e., SPAD (Soil Plant Analysis Development), and UAV-based NDVI (Unmanned Aerial Vehicle-based Normalized Difference Vegetation Index) were used to monitor maize yield based on different filed trials in eastern part of Hungary. Our study aimed to examine the utilization of PrA technology specifically SPAD and UAV-based NDVI measurements for monitoring maize GY under irrigated and rainfed experimental setups in Hungary with varied nitrogen treatment for the year 2022. The results showed that the SPAD increased in all treatments (14.7 %; p < 0.05) from V6–V8 in the rainfed treatments, decreased significantly (p < 0.05) by 13.9 % (R1) and 30.6 % (R3). However, implementation of irrigation significantly increased the SPAD values in majority of treatments. Also, results reveal that, under irrigated and rainfed conditions the highest UAV-based NDVI value (0.703, 0.642) was obtained in V12 (A120 treatment) and highest NDVI value (0.728, 0.662) was obtained in Vn (A120 treatment). Remarekedly, irrigation led to significant differences (p < 0.05) of UAV-based NDVI values compared with none irrigated. On the other hand, implementation of 120 kg N ha−1 before sowing led to highest GY, especially under irrigated conditions (8.649 Mg ha−1). The overall mean GY under rainfed treatment was 6.256 Mg ha−1, while under irrigated treatment it increased by 37.2 % and reached 8.581 Mg ha−1 (p < 0.05). In conclusion, PrA technology will support farmers in making informed decisions regarding fertilization strategies and timing, which will in turn maximize yield and minimize risk.

Indoor Plant Soil-Plant Analysis Development (SPAD) Prediction Based on Multispectral Indices and Soil Electroconductivity: A Deep Learning Approach

Leaf Soil-Plant Analysis Development (SPAD) prediction is a crucial measure of plant health and is essential for optimizing indoor plant management. The deep learning methods offer advanced tools for precise evaluations but their adaptation to the heterogeneous indoor plant ecosystem presents distinct challenges. This study assesses how accurately deep neural network (DNN) predicts SPAD values in leaves on indoor plants when compared to well-established machine learning techniques, including Random Forest (RF) and Extreme Gradient Boosting (XGB). The covariates for prediction were based on low-cost multispectral and soil electro-conductivity (EC) sensors, enabling a non-destructive sensing approach. The study also strongly emphasized multicollinearity analysis quantified by the Variance Inflation Factor (VIF) and two independent indices, as well as its effect on prediction accuracy using deep and machine learning methods. DNN resulted in higher accuracy to RF and XGB, also performing better using filtered data after multicollinearity analysis based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) (R2 = 0.589, RMSE = 11.68, MAE = 9.52) in comparison to using all input covariates (R2 = 0.476, RMSE = 12.90, MAE = 10.94). Overall, DNN was proven as a more accurate prediction method than the conventional machine learning approach for the prediction of leaf SPAD values in indoor plants, despite using heterogenous plant types and input covariates.

Growth Performance, Zinc Uptake and Response of Clonal Eucalyptus to Application of Zinc in Soils of Variable Texture

ABSTRACT Eucalyptus (Eucalyptus spp.) leads to depletion of nutrients from the soil due to its fast growing nature. Therefore, a pot experiment was conducted to study the effect of zinc (Zn) application on clonal eucalyptus growth, biomass output and its uptake, as well as response of eucalyptus to Zn in soils of three textures. The treatments consisted of eight levels of Zn (0, 5, 10, 15, 20, 25, 30, and 35 mg kg−1 applied through ZnSO4.H2O) in a completely randomized design triplicated in soils of three distinct textures (loamy sand, sandy loam and sandy clay). The plant height, collar diameter, dry weight of shoot and roots, soil plant analysis development (SPAD) readings, Zn concentration and uptake by leaves, stem+branches and roots increased significantly with Zn application. Average dry weight of shoot and roots (31.93 and 15.12 g plant−1, respectively) was significantly highest at 15 mg Zn kg−1 whereas Zn concentration and uptake (18.35 mg kg−1 and 286.2 µg plant−1, respectively) by roots were highest at 20 mg Zn kg−1. The above parameters differed significantly among soils as sandy clay>sandy loam>loamy sand except Zn content and uptake by roots and SPAD values which were highest in sandy loam soil. Loamy sand and sandy loam soil exhibited higher Zn use efficiency, shoot biomass response and uptake response to applied Zn than sandy clay soil. It is concluded to apply 20 mg Zn kg−1 in light soil and 15 mg Zn kg−1 in heavy textured soil for better growth and productivity of eucalyptus.

Effects of biochar applications on growth, nutrient content and biochemical properties of Ocimum basilicum L.

This study investigated the effects of biochar treatments on the growth, nutrient content and some biochemical properties of basil. Biochars obtained from two different biomasses, rice husk (RBC) and tomato harvest waste (TBC), were applied at a dose of 2% to the growing medium consisting of a 1 : 1 soil and peat mixture. No biochar-added medium (1 : 1 soil and peat) was used as a control. The experiment was established in a completely randomized design with six replications for each treatment. At the end of the study, the height, number of lateral branches, total herb weight, and leaf weight of the plants were measured. In addition, chlorophyll contents in SPAD (Soil Plant Analysis Development), different nutrients and total phenolics contents, and antioxidant activities were analyzed. As a result of the study, the effects of biochar treatments differed depending on the biomass source. RBC significantly increased plant height, total herb weight, and leaf weight while negatively affecting the number of lateral branches. TCB did not cause any significant variation in plant height and number of lateral branches. While RBC provided a slight increase in leaf weight compared to the control, it did not cause a significant change in plant height, total herb weight, and the number of lateral branches. Except for a slight increase in K content due to RBC application, both biochar treatments did not cause a significant increase in leaf nutrient content. While RBC treatment did not cause a significant change in total phenol, it caused an increase in antioxidant activity. TBC application decreased the SPAD value from 22.4 in the control to 20.4.

Better Inversion of Wheat Canopy SPAD Values before Heading Stage Using Spectral and Texture Indices Based on UAV Multispectral Imagery

In China’s second-largest wheat-producing region, the mid-lower Yangtze River area, cold stress impacts winter wheat production during the pre-heading growth stage. Previous research focused on specific growth stages, lacking a comprehensive approach. This study utilizes Unmanned Aerial Vehicle (UAV) multispectral imagery to monitor Soil-Plant Analysis Development (SPAD) values throughout the pre-heading stage, assessing crop stress resilience. Vegetation Indices (VIs) and Texture Indices (TIs) are extracted from UAV imagery. Recursive Feature Elimination (RFE) is applied to VIs, TIs, and fused variables (VIs + TIs), and six machine learning algorithms are employed for SPAD value estimation. The fused VIs and TIs model, based on Long Short-Term Memory (LSTM), achieves the highest accuracy (R2 = 0.8576, RMSE = 2.9352, RRMSE = 0.0644, RPD = 2.6677), demonstrating robust generalization across wheat varieties and nitrogen management practices. This research aids in mitigating winter wheat frost risks and increasing yields.

Hyperspectral Estimation of SPAD Value of Cotton Leaves under Verticillium Wilt Stress Based on GWO–ELM

Rapid and non-destructive estimation of the chlorophyll content in cotton leaves is of great significance for the real-time monitoring of cotton growth under verticillium wilt (VW) stress. The spectral reflectance of healthy and VW cotton leaves was determined using hyperspectral technology, and the original spectra were processed using Savitzky–Golay (SG) smoothing, and on its basis through mean centering, standard normal variate (SG-SNV), multiplicative scatter correction (SG-MSC), reciprocal second-order differentiation, and logarithmic second-order differentiation ([lg(SG)]″) preprocessing operations. The characteristic bands were selected based on the correlation coefficient, vegetation index, successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS). The single-factor model, back propagation neural network of particle swarm optimization algorithm, and extreme learning machine (ELM) of a grey wolf optimizer (GWO) algorithm were constructed to compare and explore the ability of each model to estimate the soil plant analysis development (SPAD) value of cotton under VW stress. The results showed that spectral pretreatment could improve the correlation between characteristic bands and SPAD values. SG-MSC and SG-SNV showed better changes in the five pretreatments, and the maximum correlation coefficients of healthy and VW cotton leaves were higher than 0.74. Compared with SPA, the accuracy of model estimation based on CARS-extracted characteristic bands was higher, and the estimation accuracy of the multi-factor model was better than that of the single-factor model under each pretreatment. For healthy cotton leaves, [lg(SG)]″–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.956 and 0.887, respectively. For VW cotton leaves, SG-MSC–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.832 and 0.824, respectively. Therefore, the GWO–ELM model constructed under different pretreatments combined with characteristic extraction methods can be used for the estimation of leaf SPAD values under VW stress to dynamically monitor VW stress in cotton and provide a theoretical reference for precision agriculture.

DYNAMICS OF LEAD TOLERANCE IN TOBACCO (NICOTIANA TABACUM L.) GENOTYPES

Lead nitrate has reports of significantly inhibiting plant growth. Early exploration of the genotypic difference for lead nitrate stress in tobacco has started. The presented study had eight tobacco genotypes subjected to 200 μM lead nitrate (Pb [NO3]2) stress in a hydroponic culture. Lead stress treatment to plants for 14 days had data recording at three times intervals of stressed plants. Assessing photosynthetic and antioxidant enzymes’ activities was in a time series order of one day, seven days, and 14 days. One-day, seven-day, and fourteen-day-old seedlings gained treatment of 200 μM lead nitrate stress and control. Soil Plant Analysis Development (SPAD) values for most genotypes decreased, while oxidant and anti-oxidant enzymes increased activity. Chlorophyll-a, chlorophyll-b, and total chlorophyll evaluated after lead nitrate toxicity showed reduced activity in studied tobacco genotypes compared with control as time passed. All chlorophyll contents, i.e., chlorophyll a, b, and total chlorophyll, declined with a longer span in lead nitrate solution. Genotype QVA-20 could benefit lead-salt tolerance and susceptible genotype ‘long chang’ cigarette based on chlorophyll content and SPAD values. Chlorophyll a capacity decreased as lead exposure to plants increased, but chlorophyll b increased in all genotypes on the 15th day. The MDA (malondialdehyde) content increased in all tobacco genotypes with increased lead nitrate exposure. Meanwhile, SOD (superoxide dismutase) contents decreased in genotypes RG-8, E1, and X6 with increased time, but POD (peroxidase) contents increased in all genotypes on the 14th day. Genotypes RG-8, E1, and X6 proved considerably tolerant of lead toxicity at 200 µM.

Citrus Canopy SPAD Prediction under Bordeaux Solution Coverage Based on Texture- and Spectral-Information Fusion

Rapid and nondestructive prediction of chlorophyll content and response to the growth of various crops using remote sensing technology is a prominent topic in agricultural remote sensing research. Bordeaux mixture has been extensively employed for managing citrus diseases, such as black star and ulcer disease. However, the presence of pesticide residues in Bordeaux mixture can significantly modify the spectral response of the citrus canopy, thereby exerting a substantial influence on the accurate prediction of agronomic indices in fruit trees. In this study, we used unmanned aerial vehicle (UAV) multispectral imaging technology to obtain remote sensing imagery of Bordeaux-covered citrus canopies during the months of July, September, and November. We integrated spectral and texture information to construct a high-dimensional feature dataset and performed data downscaling and feature optimization. Furthermore, we established four machine learning models, namely, partial least squares regression (PLS), ridge regression (RR), ridge, random forest (RF), and support vector regression (SVR). Our objectives were to identify the most effective prediction model for estimating the SPAD (soil plant analysis development) value of Bordeaux-covered citrus canopies, assess the variation in prediction accuracy between fused features and individual features, and investigate the impact of Bordeaux solution on the spectral reflectance of the citrus canopy. The results showed that (1) the impact of Bordeaux mixture on citrus canopy reflectance bands ranked from the highest to the lowest as follows: near-infrared band at 840 nm, red-edge band at 730 nm, blue band at 450 nm, green band at 560 nm, and red band at 650 nm. (2) Fused feature models had better prediction ability than single-feature modeling, with an average R2 value of 0.641 for the four model test sets, improving by 0.117 and 0.039, respectively, compared with single-TF (texture feature) and -VI (vegetation index) modeling, and the test-set root-mean-square error (RMSE) was 2.594 on average, which was 0.533 and 0.264 lower than single-TF and -VI modeling, respectively. (3) Multiperiod data fusion effectively enhanced the correlation between features and SPAD values and consequently improved model prediction accuracy. Compared with accuracy based on individual months, R improved by 0.013 and 0.011, while RMSE decreased by 0.112 and 0.305. (4) The SVR model demonstrated the best performance in predicting citrus canopy SPAD under Bordeaux solution coverage, with R2 values of 0.629 and 0.658, and RMSE values of 2.722 and 2.752 for the training and test sets, respectively.

Multivariate analysis and genetic dissection of staygreen and stem reserve mobilisation under combined drought and heat stress in wheat (Triticum aestivum L.).

Introduction: Abiotic stresses significantly reduce crop yield by adversely affecting many physio-biochemical processes. Several physiological traits have been targeted and improved for yield enhancement in limiting environmental conditions. Amongst them, staygreen and stem reserve mobilisation are two important mutually exclusive traits contributing to grain filling under drought and heat stress in wheat. Henceforth, the present study was carried out to identify the QTLs governing these traits and to identify the superiors' lines through multi-trait genotype-ideotype distance index (MGIDI) Methods: A mapping population consisting of 166 recombinant inbred lines (RILs) developed from a cross between HD3086 and HI1500 was utilized in this study. The experiment was laid down in alpha lattice design in four environmental conditions viz. Control, drought, heat and combined stress (heat and drought). Genotyping of parents and RILs was carried out with 35K Axiom® array (Wheat breeder array). Results and Discussion: Medium to high heritability with a moderate to high correlation between traits was observed. Principal component analysis (PCA) was performed to derive latent variables in the original set of traits and the relationship of these traits with latent variables.From this study, 14 QTLs were identified, out of which 11, 2, and 1 for soil plant analysis development (SPAD) value, leaf senescence rate (LSR), and stem reserve mobilisation efficiency (SRE) respectively. Quantitative trait loci (QTLs) for SPAD value harbored various genes like Dirigent protein 6-like, Protein FATTY ACID EXPORT 3, glucan synthase-3 and Ubiquitin carboxyl-terminal hydrolase, whereas QTLs for LSR were found to contain various genes like aspartyl protease family protein, potassium transporter, inositol-tetrakisphosphate 1-kinase, and DNA polymerase epsilon subunit D-like. Furthermore, the chromosomal region for SRE was found to be associated with serine-threonine protein kinase. Serine-threonine protein kinases are involved in many signaling networks such as ABA mediated ROS signaling and acclimation to environmental stimuli. After the validation of QTLs in multilocation trials, these QTLs can be used for marker-assisted selection (MAS) in breeding programs.

Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species

Mangroves have important roles in regulating climate change, and in reducing the impact of wind and waves. Analysis of the chlorophyll content of mangroves is important for monitoring their health, and their conservation and management. Thus, this study aimed to apply four regression models, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Partial Least Squares (PLS) and Adaptive Boosting (AdaBoost), to study the inversion of Soil Plant Analysis Development (SPAD) values obtained from near-ground hyperspectral data of three dominant species, Bruguiera sexangula (Lour.) Poir. (B. sexangula), Ceriops tagal (Perr.) C. B. Rob. (C. tagal) and Rhizophora apiculata Blume (R. apiculata) in Qinglan Port Mangrove Nature Reserve. The accuracy of the model was evaluated using R2, RMSE, and MAE. The mean SPAD values of R. apiculata (SPADavg = 66.57), with a smaller dispersion (coefficient of variation of 6.59%), were higher than those of C. tagal (SPADavg = 61.56) and B. sexangula (SPADavg = 58.60). The first-order differential transformation of the spectral data improved the accuracy of the prediction model; R2 was mostly distributed in the interval of 0.4 to 0.8. The accuracy of the XGBoost model was less affected by species differences with the best stability, with RMSE at approximately 3.5 and MAE at approximately 2.85. This study provides a technical reference for large-scale detection and management of mangroves.

Evaluation of the susceptibility to green stem disorder in soybeans [Glycine max (L.) Merr.] with vegetative storage protein accumulation

ABSTRACT Green stem disorder (GSD) is an important agronomical problem in soybean production because it delays leaf and stem senescence and complicates the harvest. However, a rapid and precise diagnosis of GSD has not yet been established. In this study, the effect of depodding on GSD and vegetative storage protein (VSP) accumulation was investigated in GSD-susceptible cultivar ‘Tachinagaha (Tc)’ and GSD-resistant experimental line ‘Touhoku 129 (Th)’ under two different (early and late) sowing dates in 2020 and 2021. Intact Tc plants showed relatively severe GSD at early sowing in 2020 and late sowing in 2021, whereas intact Th plants showed little GSD at both sowing dates and in both years. Meanwhile, depodding reproducibly induced GSD and increased stem weight for both Tc and Th. The relative VSP content peaked 14–21 days after R3 (DAR3) in intact plants and increased afterward in depodded plants. The relative VSP content differed at 28 DAR3 between intact and depodded plants, which was earlier than the timing when SPAD (soil plant analysis development) values differed, suggesting that VSP accumulation might be a better indicator of GSD than the SPAD value. The present study will contribute to the development of tools for diagnosing GSD.

Effects of Different Soil Water and Heat Regulation Patterns on the Physiological Growth and Water Use in an Apple–Soybean Intercropping System

In this study, a typical apple–soybean intercropping system was used to analyze the effects of different soil water and heat regulation modes on the spatial distribution of the soil water content (SWC), photosynthetic physiological characteristics, and growth. Three maximum irrigation levels [50% (W1), 65% (W2), and 80% (W3) of field capacity (FC)] and two mulching intervals [from seedling to podding stage (M1) and during the full stage (M2) of soybeans] were tested. The results showed that the SWC of W3M2 was the highest, while the W2M1 and W1M2 treatments used more deep soil water. Irrigation increased the chlorophyll content, net photosynthesis, and transpiration rate of leaves in the agroforestry system. In addition, the net photosynthetic rate of leaves under the W2 irrigation level increased after mulch removal in the later growth stage. At W1 and W2 irrigation levels, the soybean yield of half-stage mulching was 0.85–15.49% higher than that of full-stage mulching. Multiple regression analysis showed that grain yield under the W3M2 treatment reached the maximum value of the fitting equation. The photosynthetic rate, water use efficiency, and grain yield under W2M1 reached 71–86% of the maximum value of the fitting equation, with the largest soil plant analysis development value. To effectively alleviate water competition in the apple–soybean intercropping system, our results suggest adoption of the 80% FC upper irrigation limit (W3) combined with soybean M2 treatment in young apple trees–soybean intercropping system during water abundant years. In addition, adoption of the 65% FC upper irrigation limit (W2) combined with the soybean M1 treatment in water deficit years could effectively improve soil water, heat environment, and promote growth.

Effect of seed biopriming with selected endophytes on the growth and chilling tolerance of rice plants.

The aim of this study was to develop an efficient bioinoculant for amelioration of adverse effects from chilling stress (10°C), which are frequently occurred during rice seedling stage. Seed germination bioassay under chilling condition with rice (Oryza sativa L.) cv. Tainan 11 was performed to screen for plant growth-promoting (PGP) bacteria among 41 chilling-tolerant rice endophytes. And several agronomic traits were used to evaluate the effects of bacterial inoculation on rice seedling, which were experienced for 7-d chilling stress in walk-in growth chamber. The field trials were further used to verify the performance of potential PGP endophytes on rice growth. A total of three endophytes with multiple PGP traits were obtained. It was demonstrated that Pseudomonas sp. CC-LS37 inoculation led to 18% increase of maximal efficiency of Photosystem II (PSII) after 7-d chilling stress and 7% increase of chlorophyll a content, and 64% decline of malondialdehyde content in shoot after 10-d recovery at normal temperature in walk-in growth chamber. In field trial, biopriming of seeds with strain CC-LS37 caused rice plants to increase shoot chlorophyll soil plant analysis development values (by 2.9% and 2.5%, respectively) and tiller number (both by 61%) under natural climate and chilling stress during the end of tillering stage, afterward 30% more grain yield was achieved. In conclusion, strain CC-LS37 exerted its function in increase of tiller number of chilling stress-treated rice seedlings via improvement of photosynthetic characteristics, which in turn increases the rice grain yield. This study also proposed multiple indices used in the screening of potential endophytes for conferring chilling tolerance of rice plants.

Integrated use of organic amendments increased mungbean (Vigna radiata (L.) Wilczek) yield and its components compared to inorganic fertilizers

AbstractRainfall variability, low soil organic matter content, and costly inorganic fertilizers are the major agricultural constraints in Sub‐Saharan Africa. Integrated use of compost and manure is essential for sustaining soil fertility and increasing crop productivity. This study was conducted to evaluate the combined effects of compost and animal manure on mungbean growth and yield. The 12 treatments consisted of control, recommended dose of nitrogen, phosphorus, and potassium (NPK), 5 ton ha−1 of compost, 10 ton ha−1 of poultry, 10 ton ha−1 of cattle, and 10 ton ha−1 of sheep manure, and six combinations of organic amendments with 50% of their applied rate alone. These treatments were laid out in a randomized complete block design with six replications. Application of cattle manure at 10 ton ha−1 significantly increased mungbean seed yield by 66% and 84% compared to the recommended rate of NPK and control treatments, respectively. Similar observations were made on stem diameter, total pod weight, and number of seeds per pod. Plants amended with compost had the highest number of ramifications and number of pods than NPK fertilized plants, which recorded (9±) ramifications and (27±) pods per plant. On average, integrated use of 5 ton ha−1 of poultry manure + 5 ton ha−1 of cattle manure had the highest soil plant analysis development values, though not significantly different from NPK fertilizers. These results suggest that application of organic amendments could be an alternative to costly and inaccessible inorganic fertilizers for improving mungbean productivity under low‐input agriculture systems.

Genetic variation in nitrogen-use efficiency and its associated traits in dryland winter wheat (Triticum aestivum L.) cultivars released from the 1940s to the 2010s in Shaanxi Province, China.

Improving the nitrogen-use efficiency (NUE) of wheat can help mitigate the problems of poor soil fertility under dryland conditions. We conducted field experiments using three nitrogen (N) fertilization levels (0, 120, and 180 kg ha-1 ) applied to eight dryland wheat cultivars to assess NUE and its associated traits. The grain yield significantly increased with the improvement in variety, mainly as a result of a substantial increase in 1000-grain weight and harvest index. Modern wheat varieties have stabilized at an optimal plant height and exhibited improved performance in terms of NUE, partial N productivity, N harvest index, and grain protein content compared to older varieties. The NUE of wheat gradually increased with variety replacement. The net photosynthesis rate of the flag leaves in the filling stage improved with the year of cultivar release; Increasing soil-plant analysis development (SPAD) values of flag leaves in the flowering and filling stages were observed over time, with the flag leaves of modern varieties showing a high chlorophyll content in the filling stage. Additionally, the principal component analysis showed that the SPAD value, grain number per unit area, transpiration rate, leaf area, and grain protein content positively contributed to the clustering of the N180 and modern cultivars (from the 2000s to 2010s). Overall, high levels of N application did not significantly improve the NUE of wheat. However, modern wheat varieties can optimize N distribution, increase flag leaf photosynthetic capacity, and improve photosynthesis ability, thus enhancing NUE to achieve high yields under a suitable level of N supply. © 2022 Society of Chemical Industry.

Reducing and Delaying Nitrogen Recommended by Leaf Critical SPAD Value Was More Suitable for Nitrogen Utilization of Spring Wheat under a New Type of Drip-Irrigated System

Timely and accurate judgment of the nitrogen nutritional status of crops is the key to develop an optimal nitrogen application strategy. However, the evaluation criteria of nitrogen nutrition and nitrogen application strategies at each growth stage of wheat are not clear for the new type of drip-irrigated spring wheat system, TR6S (where one drip tube serves six rows of wheat, with a row spacing (RS) of 10 cm, inter-block space (IBS) of 25 cm and the lateral spacing (LS) of 80 cm, which achieved a lower drip-tube input and higher profit compared with the traditional planting system in Xinjiang). Therefore, we studied the recommendation mechanism of nitrogen fertilizer in different growth stages of wheat based on the critical SPAD values of leaves under TR6S. We set four nitrogen treatments (N1 (300 kg ha−1), N2 (270 kg ha−1), N3 (240 kg ha−1) and N4 (0 kg ha−1)) during two spring wheat growth seasons. The results revealed that the correlation coefficient (r2) between SPAD (soil plant analysis development) value and plant nitrogen content in the middle of first top leaf (L1-M) of wheat was higher than that in other leaf types and leaf positions under TR6S. A quadratic function relationship existed between a SPAD value of L1-M and grain yield. The critical SPAD values at the jointing, booting, anthesis, early milk, and late milk stages were 37.34, 39.40, 42.25, 45.57, and 35.91, respectively. In addition, through the establishment of the nitrogen application recommendation model for various wheat growth stages based on the critical SPAD value, the recommended optimal nitrogen application rates at jointing, booting, anthesis, early milk, and late milk stages were observed to be 69.4, 80.0, 90.8, 44.0, and 6.0 kg ha−1, respectively. This recommended nitrogen application strategy exhibited a better parallel relationship with the nitrogen nutrition index (NNI) of each growth period than the conventional nitrogen application strategy. Therefore, it was more in line with the actual absorption and utilization of nitrogen in wheat of TR6S. In conclusion, the SPAD values of L1-M could be relatively more accurate to evaluate the nitrogen nutrition status of wheat. Compared to traditional nitrogen application strategy, reducing and delaying nitrogen application, recommended based on the leaf SPAD model, was more suitable for nitrogen utilization under TR6S. The results can be applied in other arid and semiarid regions.