Quality Of Corn Research Articles

Impacts of agroforestry systems and intercropping on the growth, production, and seed quality of corn and beans

Impacts of agroforestry systems and intercropping on the growth, production, and seed quality of corn and beans

GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize.

The quality of corn kernels is crucial for their nutritional value, making the enhancement of kernel quality a primary objective of contemporary corn breeding efforts. This study utilized 260 corn inbred lines as research materials and assessed three traits associated with grain quality. A genome-wide association study (GWAS) was conducted using the best linear unbiased estimator (BLUE) for quality traits, resulting in the identification of 23 significant single nucleotide polymorphisms (SNPs). Additionally, nine genes associated with grain quality traits were identified through gene function annotation and prediction. Furthermore, a total of 697 quantitative trait loci (QTL) related to quality traits were compiled from 27 documents, followed by a meta-QTL analysis that revealed 40 meta-QTL associated with these traits. Among these, 19 functional genes and reported candidate genes related to quality traits were detected. Three significant SNPs identified by GWAS were located within the intervals of these QTL, while the remaining eight significant SNPs were situated within 2 Mb of the QTL. In summary, the findings of this study provide a theoretical framework for analyzing the genetic basis of corn grain quality-related traits and for enhancing corn quality.

Storage temperature affects metabolism of sweet corn

Temperature is one of the critical factors affecting the postharvest storage quality of sweet corn, but effects of storage temperatures have not been elucidated at the molecular level. In this study, the effects of storage at 0 °C, 4 °C and 20 °C, on quality and associated effects on the transcriptome and metabolome were investigated. Compared with storage at 20 °C, cobs stored at 0 °C and 4 °C had up-regulated expression of genes associated with cell wall disassembly (PG, PE, BMY, β-Gal) and down-regulated expression of genes involved in lignin synthesis, the up-regulation of SPP, SPS and SUS involved in sucrose synthesis, and the down-regulation of IN, FK, HXK and PFK involved in sucrose catabolism. Most of the genes involved in ethylene biosynthesis and signaling were inhibited at 0 °C, except for ERFs, while abscisic acid synthesis and signal transduction were most active at this temperature. Near-freezing temperature also maintains the integrity of the cell membrane by inhibiting the expression of PLD, PLC and LOX, which encode membrane lipid-degrading enzymes. Metabolomics and combined analysis showed that the contents of lysophospholipid, glutathione and L-ascorbic acid in sweet corn increased at low temperatures. These results provide insight into the effects of low temperatures on sweet corn quality.

Evaluation of Seed Quality of Grain Corn Varieties through Accelerated Ageing

In an accelerated ageing test carried out to evaluate seed quality two hybrid grain corn varieties, 4546 and 888, were subjected to ageing conditions to assess their tolerance. The objective was to identify any differences between the varieties in their ability to maintain seed quality under accelerated ageing conditions. These tests simulate and hasten the natural ageing process of seeds, providing insight into their performance during storage over time and under adverse conditions. Following the ageing process, factors such as germination rate, vigour, and overall seed quality were assessed. The seeds of hybrid grain corn varieties 4546 and 888 were exposed to accelerated ageing by maintaining them at 40°C and 100% relative humidity in a growth chamber. Evaluations were conducted at intervals of 0, 48, 96, and 144 hours. The overall results indicated that seed quality in grain corn deteriorates following accelerated ageing treatment. Variety 4546 showed a rapid decrease in germination, germination rate, and seedling vigour throughout the testing period. Both varieties experienced an increase in moisture content from 11% to 20% during the ageing process. Additionally, the electrical conductivity of seed leachate increased for both varieties as the testing progressed. The experiment concluded that the 888 grain corn variety outperformed 4546 in all evaluated parameters. The 4546 variety was found to be highly sensitive to accelerated ageing.

Poultry manure-urea fertilizer potentially influences nitrogen use efficiency, nutritional quality, and yield of baby corn (Zea mays L.)

Baby corn (Zea mays L.) is an immature type of corn harvested when the silk length becomes 2–3 inches long and consumed as raw or cooked. However, in recent years the use of organo-mineral fertilizers (OMFs) has been increasing due to their ability to improve crop production and soil health. That's why a pot experiment was carried out to determine the effects of poultry manure-urea (PMU) on baby corn production, nutritional value, nitrogen (N) use efficiency, and soil health compared to commercial urea following a completely randomized design (CRD) with four replications. Here, 3 N-fertilizer sources, viz., urea, PMU-L (low N: 14 %), and PMU-H (High N: 24 %) were applied at 50, 75, and 100 % of the recommended dose of N. Application of PMU fertilizer significantly influenced plant height, yield, nutritional quality and N use efficiency of baby corn. Significantly greater leaf chlorophyll, cob, and fodder protein content were found in PMU-treated plants than in urea. The use of PMU increased cob and fodder production by approximately 11 and 13 %, respectively compared to urea. About 25 % higher N uptake, leading to a 28 % greater N use efficiency by baby corn was found in PMU-treated plants than in urea-treated ones. Besides that, the amount of soil organic carbon (SOC) was slightly increased due to the application of PMU. Results showed that Pb, Cd, and Cr concentrations in PMU treated plants were below the WHO permissible limit.

Chemical Characteristics of Fermented Local Waxy White Corn as an Effort to Improve the Quality of Bose Corn

Chemical Characteristics of Fermented Local Waxy White Corn as an Effort to Improve the Quality of Bose Corn

Humic Acid Foliation and Nutrient Levels Concerning Yield and Quality of Sweet Corn (Zea mays L. Convar. Saccharata)

Humic Acid Foliation and Nutrient Levels Concerning Yield and Quality of Sweet Corn (Zea mays L. Convar. Saccharata)

Effects of Restricted Irrigation and Straw Mulching on Corn Quality, Soil Enzyme Activity, and Water Use Efficiency in West Ordos

Groundwater overexploitation in West Ordos necessitates sustainable irrigation practices. This study evaluated three irrigation levels—W1 (3300 m3 · ha−1), W2 (2850 m3 · ha−1), and W3 (2400 m3 · ha−1)—by modifying the wide-width planting pattern of maize. Additionally, two levels of straw mulch were analyzed: F1 (9000 kg · ha−1) and F2 (no mulch). The study aimed to investigate the effects of these treatments on corn growth dynamics, soil water temperature, soil enzyme activity, yield, grain quality, and water use efficiency. The results indicated a decline in growth indices, enzyme activities, grain quality, and yield under the limited irrigation levels W2 and W3 compared to W1. The highest corn yields were observed with W1F1 (6642.54 kg · ha−1) and W2F1 (6602.38 kg · ha−1), with the latter showing only a 0.6% decrease. Notably, water use efficiency in the W2F1 treatment improved by 4.69%, 12.08%, 10.27%, 12.59%, and 12.96% compared to W1F1, W3F1, W1F2, W2F2, and W3F2, respectively. Straw mulch (F1) significantly elevated the soil temperature, increasing the effective accumulated temperature during the growth period by 10.11~85.79 °C, and boosted the soil enzyme activity by 10–25%. Under limited irrigation, the W2 (2850 m3 · ha−1) and F1 (9000 kg · ha−1 straw) treatments achieved the highest water productivity of 2.48 kg·m⁻3, maintaining a high yield of 6602.38 kg · ha−1 while preserving nutrients essential to the corn’s quality. This approach presents a viable strategy for wide-width corn planting in groundwater-depleted regions, offering a scientifically grounded and sustainable water management solution for efficient corn production in West Ordos.

Planting time and seeding rate impact insect feeding, ear rots, and forage nutritive value in silage corn

AbstractPlanting date and seeding rate affect the microclimate within a crop field. They can influence lepidopteran insect infestation and fungal infections and are an important part of crop management strategy. Altering planting date and seeding rate can also influence yield and quality of silage corn (Zea mays L.). Field research was conducted to identify optimum planting time and seeding rate to minimize insect feeding, ear rot infections, and mycotoxin accumulation in silage corn without compromising yield and quality. Replicated field trials were conducted across multiple site‐years in Michigan with three planting dates (early: April 25–May 10; mid: May 11–25; and late: May 26–June 10) and/or four seeding rates (ranging from 69,160 to 113,620 seeds ha−1, in increments of 14,800 seeds ha−1). Mid‐planting yielded 12%–15% less forage than early and late plantings, partly due to greater insect feeding injury and fungal infections observed in the former. Neutral detergent fiber digestibility, starch, and crude protein concentration were greatest for early planting. Greater predicted milk per hectare and milk per megagram for early planting also indicated superior silage quality. Increasing seeding rate increased insect feeding and ear rot injury only when severity was >5% and >15%, respectively. The impact of increasing seeding rate on dry forage yield was specific to each site‐year. Overall, results showed that early planting of silage corn helps to escape insect and disease pressure and provides better yield and quality, while seeding rate response is variable and dependent on the field environment.

PENGARUH KONSENTRASI EKSTRAK DAUN KELOR (Moringa oleifera L.) TERHADAP MORTALITAS Sitophilus zeamais Motsch.

Corn is the strategic commodity in Indonesia. The quality of many domestic corn does not meet feed factory standards. High yield losses icause of damage due to warehouse pests. Sitophilus zeamais Motsch or powder beetle is a warehouse pest that causes seeds to become hollow, break quickly and disintegrate into flour. Sitophilus zeamais Motsch pest control still relies on synthetic pesticides. The use of synthetic pesticides will cause various negative impacts such as pest resistance, residue, resurgencies, high costs and environmental pollution. One alternative for controlling Sitophilus zeamais Motsch is to use natural ingredients, namely Moringa. Moringa leaves contain phenol, hydroquinone, flavonoids, steroids, triterpenoids, tannins, alkaloids and saponins. The experiment began by drying 10 grams of Moringa leaves. then puree using a blender and continue with maceration. 200, 400, 600, 800, and 1000 mg of Moringa leaf powder were dissolved in 1 L of distilled water solution and left for 24 hours. After 24 hours, the solution was filtered 3 times so that all the substances contained in the Moringa leaves are extracted. The insecticide was tested by observing the number of S. zeamais pests that died when given Moringa leaf extract. The mortality data that was obtained was analyzed using ANOVA, if there was a real effect then continued using the 5% DMRT test. Determination of LC50 using a probit test. Moringa extract can function as a vegetable insecticide for S.zeamais. The higher concentration of Moringa leaf extract can increase the mortality of S.zeamais

Effects of slightly acidic electrolyzed water on the quality and antioxidant capacity of fresh red waxy corn during postharvest cold storage

Effects of slightly acidic electrolyzed water on the quality and antioxidant capacity of fresh red waxy corn during postharvest cold storage

Spectral Index-Based Estimation of Total Nitrogen in Forage Maize: A Comparative Analysis of Machine Learning Algorithms

Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data.

A corn seed spacing detection method based on image stitching and YOLOX

Corn seed spacing is fundamental for detecting doubles and misses and calculating the seed spacing conformity and variation. These factors are essential for optimizing the corn seeding operation system and improving the quality of corn seeding. This paper proposed a corn seed spacing detection method based on image stitching and YOLOX in the seedbed. The image stitching algorithm based on the “speed-rate” linkage could generate a seedbed panorama by stitching the images obtained from an industrial camera. As a result, the seedbed panorama could present the overall distribution of seeds in the detection region. The YOLOX-based seed spacing detection algorithm was utilized to detect the seeds in the seedbed panorama, determine the position coordinates of each seed, as well as compute the seed spacing. This paper established a corn seed spacing detection system based on the above method and constructed the soil trough and field experiments. The soil trough experimental results indicated that: (1) The seedbed panorama generated by the image stitching algorithm based on the “speed-rate” linkage could accurately reflect the seedbed information; compared to other image stitching algorithms based on feature extraction, the image stitching algorithm based on the “speed-rate” linkage had obvious advantages in stitching speed and completeness of seed spacing information. (2) With a score threshold of 0.5, YOLOX had an accuracy of 96.67 %, a recall of 97.89 %, and an F1 of 97 %, which could be adapted for seed detection in seedbed images. (3) The missed detection rate of the YOLOX-based seed spacing detection algorithm in the seedbed panorama was 3.8 %. Compared to the seed spacing of the seedbed panorama was measured employing ImageJ (IM), the average error of the YOLOX-based seed spacing detection algorithm was 1.99 %. Comparing the seed spacing of the seedbed panorama obtained by the YOLOX-SSDA (YD) with IM, the average distinction between the two detection results was 1.53 mm, and the average detection error for seed spacing was 2.79 %. The field experimental results indicated that at different forward speeds (6 km/h, 8 km/h, and 10 km/h), corn seed spacing detection accuracy was 95.5 %, 93.2 %, and 90.6 %, respectively. Furthermore, the feasibility and accuracy of the corn seed spacing detection method in the seedbed were demonstrated. This research positively impacted the advancement of seeding detection technology and the enhancement of corn seeding quality.

Effect of Different Nutrient Combinations on Quality of Sweet Corn (Zea mays L. Var. Saccharata.)

A field experiment was conducted at Post Graduate Instructional Farm, Mahatma Phule Krishi Vidyapeeth, Rahuri, India, during Rabi season 2020. The trial was laid out in Randomised Block Design with nine treatments replicated thrice. The soil of the experimental plot was clay loamy, medium in available nitrogen, medium in available phosphorus, and high in available potassium. The sowing was done on 24 November 2020. The general recommended dose of fertiliser applied was 120:60:40 N: P2O5: K2O kg ha-1+ 10 t FYM ha-1. Application of 125% general recommended dose of fertiliser along with a foliar spray of 2% 19:19:19 NPK at 30 and 45 DAS recorded significantly higher crude protein content in cob and stover (11.12%, 5.18%), Protein Yield (1878 kg ha-1).

Integrated analysis of transcriptomic and metabolomic data reveals how slurry ice treatment affects sugar metabolism in sweet corn (Zea mays L. var saccharata) during cold storage

AbstractSweet corn rapidly loses its sweetness, along with other nutrients, once it is harvested, which reduces its economic value. The present study was conducted to elucidate the regulatory mechanisms by which a rapid cooling slurry ice (SI) treatment followed by storage at 0 ± 0.5°C extends the postharvest quality of sweet corn relative to slower direct cooling at 0 ± 0.5°C. In this study, we found that SI treatment can maintain the appearance quality of sweet corn and inhibit grain shrinkage, respiration, and ethylene release. SI treatment delayed the aging of corn by regulating the expression levels of genes and the levels of metabolites. SI treatment also maintains its carbohydrate content, which is mainly related to starch and sucrose metabolism, glycolysis, citric acid cycle, pentose phosphate pathway, and cutin biosynthesis. The results provide a possible target for improving the postharvest quality of sweet corn.

Genetic analysis and QTL mapping for pericarp thickness in maize (Zea mays L.)

Proper pericarp thickness protects the maize kernel against pests and diseases, moreover, thinner pericarp improves the eating quality in fresh corn. In this study, we aimed to investigate the dynamic changes in maize pericarp during kernel development and identified the major quantitative trait loci (QTLs) for maize pericarp thickness. It was observed that maize pericarp thickness first increased and then decreased. During the growth and formation stages, the pericarp thickness gradually increased and reached the maximum, after which it gradually decreased and reached the minimum during maturity. To identify the QTLs for pericarp thickness, a BC4F4 population was constructed using maize inbred lines B73 (recurrent parent with thick pericarp) and Baimaya (donor parent with thin pericarp). In addition, a high-density genetic map was constructed using maize 10 K SNP microarray. A total of 17 QTLs related to pericarp thickness were identified in combination with the phenotypic data. The results revealed that the heritability of the thickness of upper germinal side of pericarp (UG) was 0.63. The major QTL controlling UG was qPT1-1, which was located on chromosome 1 (212,215,145–212,948,882). The heritability of the thickness of upper abgerminal side of pericarp (UA) was 0.70. The major QTL controlling UA was qPT2-1, which was located on chromosome 2 (2,550,197–14,732,993). In addition, a combination of functional annotation, DNA sequencing analysis and quantitative real-time PCR (qPCR) screened two candidate genes, Zm00001d001964 and Zm00001d002283, that could potentially control maize pericarp thickness. This study provides valuable insights into the improvement of maize pericarp thickness during breeding.

Postharvest ripening of newly harvested corn: Weakened interactions between starch and protein

The effects of postharvest ripening of corn on the mechanisms of starch and protein interactions were investigated using molecular dynamics and several chemical substances. Sodium dodecyl sulfate (SDS) treatment all significantly affected the starch content, molecular weight of proteins, relative crystallinity, pasting characteristics and dynamic viscoelasticity in samples before and after postharvest ripening. In the corn that had not undergone postharvest ripening, there were also significant electrostatic interactions and hydrogen bonds between starch and protein. In addition, molecular dynamics had demonstrated that the forces between starch and protein in corn were mainly hydrophobic interactions, electrostatic interaction, and hydrogen bonds. Compared with zein, corn glutelin was more tightly bound to starch. The binding energy of starch to both proteins was reduced in after postharvest-ripened corn. This study laid a rationale for investigating the change mechanism of corn postharvest ripening quality and improving processing property and edible quality of corn.

Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis

This study addresses the challenge of effectively selecting relevant variables and providing interpretable insights in spectroscopic analysis (1100–2498 nm) of corn quality (moisture, fat, protein, and starch), incorporating variable selection techniques and explainable artificial intelligence (AI). Three variable selection algorithms were used to select 36 important variables, and through combinatorial optimization, only 11 common wavelengths were chosen. Partial least squares regression (PLSR) models were developed using the full spectral range, individual variables, and common variables. Based on the root mean square errors of prediction (RMSEP), the PLSR models with common variables outperformed individual feature variables for starch (0.22 % vs. 0.24 %) and were comparable for oil (0.05 % vs. 0.05 %), water (0.02 % vs. 0.01 %) and protein (0.12 % vs. 0.09 %). The SHapley Additive exPlanation (SHAP) method was employed to explain the PLSR model and assess the contribution of common variables. This combined approach significantly improved the accuracy and interpretability of corn prediction models, providing trust in the results and facilitating the understanding of the relationship between spectral features and corn quality attributes.

Effect of Nitrogen and Crop Geometry on Growth and Yield of Baby Corn

The field experiment was conducted to investigate the Effect of Nitrogen and Crop Geometry on Growth and Yield ofBaby Corn during Zaid season of 2023 at the Crop Research Farm, Department of Agronomy, Naini Agricultural Institute, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj (U.P.) India. The main objectives of experiment is to studythe efficacy of nitrogen and crop geometry on growth and yield of baby corn. The experiment was executed using Randomized Block Design with ten treatments and replicated thrice. The treatments combinations are T1:Nitrogen 60kg/ha + Crop Geometry 40cm × 20cm T2: Nitrogen 80kg/ha + Crop Geometry40cm × 20cm T3: Nitrogen 100kg/ha + Crop Geometry 40cm × 20cm T4: Nitrogen 60kg/ha + Crop Geometry 50cm × 20cm T5: Nitrogen 80kg/ha + Crop Geometry 50cm × 20cm T6: Nitrogen 100kg/ha + Crop Geometry 50cm × 20cm T7: Nitrogen 60kg/ha + Crop Geometry 60cm × 20cm. T8: Nitrogen 80kg/ha + Crop Geometry 60cm × 20cm T9: Nitrogen 100kg/ha + Crop Geometry 60cm × 20cm, T10: Control (RDF-60- 40-40 NPK kg/ha) are used. The result of the study revealed that the higher plant height (173.47 cm), higher plant dry weight (29.3 g/plant), number of cobs/plant (2.1), green fodder yield (33.32 t/ha), cob yield with husk (12.30 t/ha), andcob yield without husk (4.33 t/ha) were significantly showed significant difference with application of Nitrogen100kg/ha + Crop Geometry 50cm × 20cm. It was concluded that for obtaining higher yield components with better quality of baby corn with Nitrogen 100 kg/ha + Crop Geometry 50cm × 20cm was recommended.

Fine mapping and candidate gene analysis of qSRC3 controlling the silk color in maize (Zea mays L.).

Fine mapping of the maize QTL qSRC3, responsible for red silk, uncovered the candidate gene ZmMYB20, which encodes an R2R3-MYB transcription factor, has light-sensitive expression, and putatively regulates genes expression associated with anthocyanin biosynthesis. Colorless silk is a key characteristic contributing to the visual quality of fresh corn intended for market distribution. Nonetheless, the identification of Mendelian trait loci and associated genes that control silk color has been scarce. In this study, a F2 population arising from the hybridization of the single-segment substitution line qSRC3MT1 with red silk, carrying an introgressed allele from teosinte (Zea mays ssp. mexicana), and the recurrent maize inbred line Mo17, characterized by light green silk, was utilized for fine mapping. We found that the red silk trait is controlled by a semi-dominant genetic locus known as qSRC3, and its expression is susceptible to light-mediated inhibition. Moreover, qSRC3 explained 68.78% of the phenotypic variance and was delimited to a 133.2kb region, which includes three genes. Subsequent expression analyses revealed that ZmMYB20 (Zm00001d039700), which encodes an R2R3-MYB transcription factor, was the key candidate gene within qSRC3. Yeast one-hybrid and dual-luciferase reporter assays provided evidence that ZmMYB20 suppresses the expression of two crucial anthocyanin biosynthesis genes, namely ZmF3H and ZmUFGT, by directly binding to their respective promoter regions. Our findings underscore the significance of light-inhibited ZmMYB20 in orchestrating the spatial and temporal regulation of anthocyanin biosynthesis. These results advance the production of colorless silk in fresh corn, responding to the misconception that fresh corn with withered colored silk is not fresh and providing valuable genetic resources for the improvement of sweet and waxy maize.