Management of nitrogen through the use of leaf color chart (LCC) and soil plant analysis development (SPAD) or chlorophyll meter in rice under irrigated ecosystem

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.

Rice nitrogen nutrition monitoring classification method based on the convolution neural network model: Direct detection of rice nitrogen nutritional status

The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.

Spad value varies with age and leaf of maize plant and its relationship with grain yield

OBJECTIVES

A field experiment was conducted to evaluate Soil Plant Analysis Development (SPAD) value in different age and leaf of maize hybrid and correlating with grain yield. Ten maize hybrids were replicated thricely under Randomized Complete Block Design (RCBD) during winter of 2018. SPAD value was measured by SPAD 502 plus meter. At 30 days interval during vegetative stage SPAD measurement were taken from T1 (top most leaf) and T3 (2nd leaf from top leaf) leaves of five randomly selected plants from one plot and they were averaged. For reproductive phase data taken from eo (leaf attached to ear) and e2 (2nd leaf from eo leaf) leaves at 10 days intervals. Same leaves were used for entire data collection.

RESULTS

Significantly different SPAD value was observed for different age and leaves of maize during pre and post anthesis. SPAD value increase with increase in age and decrease at the time of maturity. During vegetative phase T3 leaves has more SPAD value than T1. During reproductive stage eo leaves had more SPAD than e2 leaves, so center leaf of maize contributes more to grain yield. Correlation showed that there is strong positive correlation between different stage of SPAD with grain yield.

Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries

To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available.

Computational Method for the Identification of Molecular Metabolites Involved in Cereal Hull Color Variations

Background:

Cereal hull color is an important quality specification characteristic. Many studies were conducted to identify genetic changes underlying cereal hull color diversity. However, these studies mainly focused on the gene level. Recent studies have suggested that metabolomics can accurately reflect the integrated and real-time cell processes that contribute to the formation of different cereal colors.

Methods:

In this study, we exploited published metabolomics databases and applied several advanced computational methods, such as minimum redundancy maximum relevance (mRMR), incremental forward search (IFS), random forest (RF) to investigate cereal hull color at the metabolic level. First, the mRMR was applied to analyze cereal hull samples represented by metabolite features, yielding a feature list. Then, the IFS and RF were used to test several feature sets, constructed according to the aforementioned feature list. Finally, the optimal feature sets and RF classifier were accessed based on the testing results.

Results and Conclusion:

A total of 158 key metabolites were found to be useful in distinguishing white cereal hulls from colorful cereal hulls. A prediction model constructed with these metabolites and a random forest algorithm generated a high Matthews coefficient correlation value of 0.701. Furthermore, 24 of these metabolites were previously found to be relevant to cereal color. Our study can provide new insights into the molecular basis of cereal hull color formation.

Engineering a New Chloroplastic Photorespiratory Bypass to Increase Photosynthetic Efficiency and Productivity in Rice

Over the past few years, three photorespiratory bypasses have been introduced into plants, two of which led to observable increases in photosynthesis and biomass yield. However, most of the experiments were carried out using Arabidopsis under controlled environmental conditions, and the increases were only observed under low-light and short-day conditions. In this study, we designed a new photorespiratory bypass (called GOC bypass), characterized by no reducing equivalents being produced during a complete oxidation of glycolate into CO₂ catalyzed by three rice-self-originating enzymes, i.e., glycolate oxidase, oxalate oxidase, and catalase. We successfully established this bypass in rice chloroplasts using a multi-gene assembly and transformation system. Transgenic rice plants carrying GOC bypass (GOC plants) showed significant increases in photosynthesis efficiency, biomass yield, and nitrogen content, as well as several other CO₂-enriched phenotypes under both greenhouse and field conditions. Grain yield of GOC plants varied depending on seeding season and was increased significantly in the spring. We further demonstrated that GOC plants had significant advantages under high-light conditions and that the improvements in GOC plants resulted primarily from a photosynthetic CO₂-concentrating effect rather than from improved energy balance. Taken together, our results reveal that engineering a newly designed chloroplastic photorespiratory bypass could increase photosynthetic efficiency and yield of rice plants grown in field conditions, particularly under high light.