A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels.

High-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two-dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.

Determining the geographical origin of flaxseed based on stable isotopes, fatty acids and antioxidant capacity.

BACKGROUND: Flaxseed is an economically important oilseed crop whose geographic origin is of significant interest to producers and consumers because every region may exhibit particular quality characteristics. The lipid/fatty acid method of determining the geographic origin of flaxseed has not been found to be adequate. RESULTS: To improve the discrimination rate and the geographical traceability of this crop, the chemical profiles of the flaxseed samples were characterized via lipids/fatty acids, stable isotopes, and antioxidant capacity. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were also performed. A satisfactory discrimination rate of 98.6% was obtained after combining fatty acids, stable isotopes, and antioxidant capacity to trace the origin of flaxseed from five regions in northern China. CONCLUSION: This study provides an effective method for distinguishing the geographic origin of flaxseed. © 2021 Society of Chemical Industry.

Identification of the variety of maize seeds based on hyperspectral images coupled with convolutional neural networks and subregional voting.

BACKGROUND: Maize is one of the most important food crops in the world. Many different varieties of maize seeds are similar in size and appearance, so distinguishing the varieties of maize seed is a significant research topic. This study used hyperspectral image processing coupled with convolutional neural network (CNN) and a subregional voting method to recognize different varieties of maize seed. RESULTS: First, visible and near-infrared (NIR-visible) hyperspectral images were obtained. Savitzky-Golay (SG) smoothing and first derivative (FD) were used to pretreat the raw spectra and highlight the spectral differences of samples of different varieties. Second, the region of interest (ROI) of each sample was divided into several subregions according to the shape and the number of pixels. Then, a method was proposed for reshaping the images of pixel spectra for the CNN and the training model was established. Finally, using subregional voting, one prediction result was generated from the prediction results of several original subregions in one sample. The results showed that, for six varieties of normal maize seeds, the tests identified embryoid and non-embryoid forms with 93.33% and 95.56% accuracy, respectively. For six varieties of sweet maize seeds, the test accuracy in embryoid and non-embryoid forms was 97.78% and 98.15%, respectively. CONCLUSION: The maize seed was identified accurately. The present study demonstrated that the CNN model for spectral image coupled with subregional voting represents a new approach for the identification of varieties of maize seed. © 2021 Society of Chemical Industry.

Quality differentiation of cocoa beans: implications for geographical indications.

Geographical indications may stimulate collective actions of governance for quality control, trade and marketing as well as innovation based on the use of local resources and regional biodiversity. Cocoa production, however, dominated by small family agriculture in tropical regions, has rarely made use of such strategies. This review is aimed at understanding major research interests and emerging technologies helpful for the origin differentiation of cocoa quality. Results from literature search and cited references of publications on cocoa research were imported into VOSviewer for data analysis, which aided in visualizing major research hotpots. Co-occurrence analysis yielded major research clusters which guided the discussion of this review. Observed was a consensus recognizing cocoa quality resulting from the interaction of genotype, fermentation variables and geographical origin. A classic view of cocoa genetics based on the dichotomy of 'fine versus bulk' has been reexamined by a broader perspective of human selection and cocoa genotype evolution. This new approach to cocoa genetic diversity, together with the understanding of complex microbiome interactions through fermentation, as well as quality reproducibility challenged by geographical conditions, have demonstrated the importance of terroir in the production of special attributes. Cocoa growing communities around the tropics have been clearly enabled by new omics and chemometrics to systematize producing conditions and practices in the designation of specifications for the differentiation of origin quality. © 2021 Society of Chemical Industry.

Influences of grain and protein characteristics on in vitro protein digestibility of modern and ancient wheat species.

BACKGROUND: The resistance of proteins to gastrointestinal digestion contributes to their ability to act as allergens. Near-complete digestion of protein in wheat products is important with respect to avoiding the potential immunogenic effects of undigested peptides. Five modern US wheat classes (soft red winter, hard winter, hard red spring, club and durum) including 17 wheat varieties, as well as three ancient wheat classes (spelt, emmer and einkorn) including nine wheat varieties, were analyzed for kernel hardness (KH) and flour protein characteristics, in addition to in vitro protein digestibility (IVPD) of cooked flour, flour without albumins and globulins (FWOAG), gluten, albumins, and globulins, aiming to identify the factors influencing the protein digestibility of flour. RESULTS: IVPDs of flour, FWOAG, gluten and albumins of wheat varieties ranged from 86.5% to 92.3%, 85.8% to 90.3%, 90.6% to 94.6% and 74.8% to 85.1%, respectively. The IVPD of gluten was significantly higher than the IVPDs of flour and FWOAG, indicating that non-protein components substantially affect protein digestibility. Significant differences were observed in IVPDs of flour and albumins among eight wheat classes, but not in the IVPDs of FWOAG, gluten and globulins. There were apparent differences in undigested protein bands and intensities of wheat classes with low and high flour IVPDs. KH and albumin proportion exhibited negative and positive relationships, respectively, with flour IVPD. CONCLUSION: The results of the present study demonstrate that KH, non-protein components and albumin proportion have a major influence on protein digestion and need to be considered when developing wheat cultivars with higher protein digestibility.

Identification of the volatile profiles of 22 traditional and newly bred maize varieties and their porridges by PTR-QiTOF-MS and HS-SPME GC-MS.

BACKGROUND: Low adoption of maize varieties bred to address the nutritional needs of the growing African population limits their impact. Aroma is essential in consumer preference, but has hitherto hardly been studied. We analysed the volatile organic compounds of flours and porridges of 22 maize varieties belonging to four nutritionally distinct groups, namely provitamin A maize, quality protein maize, yellow and white maize. RESULTS: Proton-transfer-reaction quadrupole ion time-of-flight mass spectrometry (PTR-QiTOF-MS) analysis generated 524 mass peaks ranging from 16.007 to 448.089 m/z. Principal component analysis separated the varieties belonging to the four groups. With headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME GC-MS), 48 volatile compounds were identified in maize flour and 21 in maize porridge, including hexane, nonane, pentanoic acid, 1-octen-3-ol, 1-hexanol, hexanal, nonanal, 2-pentylfuran and 2-heptanone. Volatile compounds such as 1,2,4-trimethyl benzene, associated with thermal degradation of carotenoids, increased in the porridge of yellow and provitamin A maize. CONCLUSION: The results indicate that PTR-QiTOF-MS and HS-SPME GC-MS combined with multivariate analysis are instrumental to study the volatile aroma compounds of different maize varieties.

Comparison of seed fatty and amino acids in edamame dried using two oven-drying methods and mature soybeans.

BACKGROUND: Edamame is a vegetable soybean (Glycine max) rich in protein with well-balanced amino acid profiles, including all the essential amino acids. It is also relatively high in oil consisting of five fatty acids. In this study, fatty and amino acid contents in oven-dried edamame and mature seeds in 54 genotypes were analyzed to help edamame breeding and relevant research. RESULTS: Significant differences (P < 0.01) between dried edamame and mature soybeans were found for all traits, suggesting that the contents of seed fatty and amino acids varied with drying method and/or growth stage. The genotypic effects were significant for amino acids in all cases and mostly for fatty acids. Fatty acid content was more variable than amino acids in both edamame and mature soybeans. The broad-sense heritability estimates of traits in mature soybeans (51.89-90.37%) were larger than those in edamame seeds (13.10-81.83%), and the estimates for amino acids were larger than those for fatty acids. Correlations between the two edamame drying methods or between the edamame and mature soybeans were significant for all fatty and amino acids with few exceptions, and the correlation coefficients for amino acids (0.36-0.89) were larger than those for fatty acids (0.29-0.81). CONCLUSIONS: Selection for both fatty and amino acids in edamame seems less effective than that in mature soybeans, and the improvement of amino acids would be more achievable than fatty acid improvement. Indirect selection through mature soybeans may benefit the edamame improvement for most of the amino acids. © 2020 Society of Chemical Industry.

Influence of starch fine structure and storage proteins on the eating quality of rice varieties with similar amylose contents.

BACKGROUND: Rice eating quality largely dictates consumer preference, and the demand for new rice varieties with excellent eating quality from farmers is increasing. Identification of factors contributing to eating quality is helpful for developing high-quality rice varieties. RESULTS: Two groups of rice with different apparent amylose content (AACs) were used in this study. One group contained four varieties with low AACs (8.8-9.4%), whereas the other contained four traditional varieties with medium AACs (17.2-17.5%). The physicochemical properties, starch fine structure and crystallinity and storage protein composition of the two groups were analyzed. We found that, in both groups, the rice varieties with high eating quality had more short-chain amylopectin, lower glutelin and prolamin content, and a higher albumin content. In addition, the low-AAC varieties produced opaque endosperms, which may result from an increased number of pores in the center of starch granules. CONCLUSIONS: Both the fine structure of starch and the storage protein composition were closely related to rice eating quality. In both groups, short branch-chain amylopectin, short-chain amylopectin [degree of polymerization (DP) 6-12], and albumin had positive effects on eating quality. By contrast, long branch-chain amylopectin, long-chain amylopectin (DP 35-60), glutelin and prolamin had adverse effects on eating quality of rice. © 2020 Society of Chemical Industry.

Physiological traits, yield, and yield components relationship in winter and spring canola.

BACKGROUND: Understanding the relationship between physiological traits with yield and yield components is an essential step towards developing high-yielding and high-quality canola (Brassica napus L.) cultivars. This study aimed to explore further the relationship between some physiological features, including radiation use efficiency (RUE), and seed yield in canola. RESULTS: Significant differences were found among cultivars regarding maximum leaf area index (LAImax ) and required days to achieve maximum LAI (DLAImax ). All cultivars obtained the minimum LAI required to intercept 90% of the incident radiation, but at different times. Some cultivars like SW102 and Shirali had the same fraction of intercepted photosynthetically active radiation (IPAR) when LAI was maximal, but SW102 had higher IPAR. This indicated that SW102 was more efficient in irradiation capacity and may have a higher photosynthesis rate when exposed to the high irradiation conditions. The average canola RUE in the current study was 3.80 and 3.63 g MJ-1 m-2 in 2014 and 2015, respectively. In general, the crop growth rate was higher in the first year than in the second year due to the fewer cloudy days and more incident radiation. CONCLUSION: Results indicated that duration of growth, crop growth rate, and harvest index were crucial for enhancing biomass and seed yield. Also, a relatively high correlation was found between the RUE and DLAImax . The cultivars that reached their maximum LAI later demonstrated higher RUE, and consequently had higher biological and seed yield. The results obtained could be used to develop an improved canola crop growth model and breeding programs. © 2021 Society of Chemical Industry.

Seed size and its rate of evolution correlate with species diversification across angiosperms.

Species diversity varies greatly across the different taxonomic groups that comprise the Tree of Life (ToL). This imbalance is particularly conspicuous within angiosperms, but is largely unexplained. Seed mass is one trait that may help clarify why some lineages diversify more than others because it confers adaptation to different environments, which can subsequently influence speciation and extinction. The rate at which seed mass changes across the angiosperm phylogeny may also be linked to diversification by increasing reproductive isolation and allowing access to novel ecological niches. However, the magnitude and direction of the association between seed mass and diversification has not been assessed across the angiosperm phylogeny. Here, we show that absolute seed size and the rate of change in seed size are both associated with variation in diversification rates. Based on the largest available angiosperm phylogenetic tree, we found that smaller-seeded plants had higher rates of diversification, possibly due to improved colonisation potential. The rate of phenotypic change in seed size was also strongly positively correlated with speciation rates, providing rare, large-scale evidence that rapid morphological change is associated with species divergence. Our study now reveals that variation in morphological traits and, importantly, the rate at which they evolve can contribute to explaining the extremely uneven distribution of diversity across the ToL.

Pseudomonas fluorescens LBUM677 differentially increases plant biomass, total oil content and lipid composition in three oilseed crops.

AIMS: Pseudomonas spp. have been widely studied for their plant growth-promoting effects. However, their capacity to promote lipid accumulation in oilseed crops is not well characterized. In this study, we evaluated the effect of Pseudomonas fluorescens LBUM677 on lipid accumulation in three oilseed crops: soybean (Glycine max), canola (Brassica napus) and corn gromwell (Buglossoides arvensis), a plant of high nutraceutical interest for its accumulation of the omega-3 stearidonic acid. METHODS AND RESULTS: Pot experiments were conducted under controlled conditions where seeds were inoculated or not with LBUM677 and plants were harvested at 4, 8 and 12 weeks. A qPCR assay specifically targeting LBUM677 was used in parallel to correlate LBUM677 soil rhizosphere competency to growth promotion and seed lipid accumulation. Total oil seed content and fatty acid composition were analysed at seed maturity. Results showed that LBUM677 was able to establish itself in the rhizosphere of the three plant species at similar levels, but it differentially increased plant biomass, total oil content and lipid composition in a plant-specific manner. CONCLUSIONS: Despite some species-specific differences observed in P. fluorescens LBUM677's effect on different crops, the strain appears to be a generalist plant growth-promoting rhizobacteria of oilseed crops. SIGNIFICANCE AND IMPACT OF THE STUDY: LBUM677 shows great potential to be used as an inoculum to promote oil yield and fatty acid accumulation in oilseed crops.

The presumed ginkgophyte Umaltolepis has seed-bearing structures resembling those of Peltaspermales and Umkomasiales.

The origins of the five groups of living seed plants, including the single relictual species Ginkgo biloba, are poorly understood, in large part because of very imperfect knowledge of extinct seed plant diversity. Here we describe well-preserved material from the Early Cretaceous of Mongolia of the previously enigmatic Mesozoic seed plant reproductive structure Umaltolepis, which has been presumed to be a ginkgophyte. Abundant new material shows that Umaltolepis is a seed-bearing cupule that was borne on a stalk at the tip of a short shoot. Each cupule is umbrella-like with a central column that bears a thick, resinous, four-lobed outer covering, which opens from below. Four, pendulous, winged seeds are attached to the upper part of the column and are enclosed by the cupule. Evidence from morphology, anatomy, and field association suggests that the short shoots bore simple, elongate Pseudotorellia leaves that have similar venation and resin ducts to leaves of living GinkgoUmaltolepis seed-bearing structures are very different from those of Ginkgo but very similar to fossils described previously as Vladimaria. Umaltolepis and Vladimaria do not closely resemble the seed-bearing structures of any living or extinct plant, but are comparable in some respects to those of certain Peltaspermales and Umkomasiales (corystosperms). Vegetative similarities of the Umaltolepis plant to Ginkgo, and reproductive similarities to extinct peltasperms and corystosperms, support previous ideas that Ginkgo may be the last survivor of a once highly diverse group of extinct plants, several of which exhibited various degrees of ovule enclosure.

Cultivar Discrimination of Single Alfalfa (Medicago sativa L.) Seed via Multispectral Imaging Combined with Multivariate Analysis.

Rapid and accurate discrimination of alfalfa cultivars is crucial for producers, consumers, and market regulators. However, the conventional routine of alfalfa cultivars discrimination is time-consuming and labor-intensive. In this study, the potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed. Three multivariate analysis methods including principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) were applied to distinguish seeds of 12 alfalfa cultivars based on their morphological and spectral traits. The results showed that the combination of morphological features and spectral data could provide an exceedingly concise process to classify alfalfa seeds of different cultivars with multivariate analysis, while it failed to make the classification with only seed morphological features. Seed classification accuracy of the testing sets was 91.53% for LDA, and 93.47% for SVM. Thus, multispectral imaging combined with multivariate analysis could provide a simple, robust and nondestructive method to distinguish alfalfa seed cultivars.

Characterization of the Heavy-Metal-Associated Isoprenylated Plant Protein (HIPP) Gene Family from Triticeae Species.

Heavy-metal-associated (HMA) isoprenylated plant proteins (HIPPs) only exist in vascular plants. They play important roles in responses to biotic/abiotic stresses, heavy-metal homeostasis, and detoxification. However, research on the distribution, diversification, and function of HIPPs in Triticeae species is limited. In this study, a total of 278 HIPPs were identified from a database from five Triticeae species, and 13 were cloned from Haynaldia villosa. These genes were classified into five groups by phylogenetic analysis. Most HIPPs had one HMA domain, while 51 from Clade I had two, and all HIPPs had good collinear relationships between species or subgenomes. In silico expression profiling revealed that 44 of the 114 wheat HIPPs were dominantly expressed in roots, 43 were upregulated under biotic stresses, and 29 were upregulated upon drought or heat treatment. Subcellular localization analysis of the cloned HIPPs from H. villosa showed that they were expressed on the plasma membrane. HIPP1-V was upregulated in H. villosa after Cd treatment, and transgenic wheat plants overexpressing HIPP1-V showed enhanced Cd tolerance, as shown by the recovery of seed-germination and root-growth inhibition by supplementary Cd. This research provides a genome-wide overview of the Triticeae HIPP genes and proved that HIPP1-V positively regulates Cd tolerance in common wheat.

Origin traceability of peanut kernels based on multi-element fingerprinting combined with multivariate data analysis.

BACKGROUND: Multi-elements have been widely used to identify the geographical origins of various agricultural products. The objective of this study was to investigate the feasibility of identifying the geographical origins of peanut kernels at different regional scales by using the multi-element fingerprinting technique. The concentrations of 20 elements [boron (B), magnesium (Mg), phosphorus (P), potassium (K), calcium (Ca), etc.] were determined in 135 peanut samples from Jilin Province, Jiangsu Province, and Shandong Province of China. Data obtained were processed by one-way analysis of variance (ANOVA), principal components analysis (PCA), k nearest neighbors (k-NN), linear discriminant analysis (LDA), and support vector machine (SVM). RESULTS: Peanut kernels from different regions had their own element fingerprints. The k-NN, LDA, and SVM were all suitable to predict peanut kernels according to their grown provinces with the total correct classification rates of 91.2%, 91.1%, and 91.1%, respectively. While SVM was the best to identify different grown cities of peanut kernels with the prediction accuracy of 91.3%, compared to 72.2% and 78.3% for k-NN and LDA, respectively. CONCLUSION: It was an effective method to identify producing areas of peanut kernels at different regional scales using multi-element fingerprinting combined with SVM to enhance regional capabilities for quality assurance and control. © 2020 Society of Chemical Industry.

Detection of sunn pest-damaged wheat grains using artificial bee colony optimization-based artificial intelligence techniques.

BACKGROUND: In this study, artificial intelligence models that identify sunn pest-damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG and 150 SDG being used for classification. Thanks to the constructed imaging setup, photos of the 300 wheat grains are obtained. Seventeen visual features of each wheat grain are extracted by image-processing techniques and evaluated in three different groups of dimension, texture and pattern as visual parameters. Artificial bee colony (ABC) optimization-based artificial neural network (ANN) and extreme learning machine (ELM) algorithms are implemented to classify the damaged wheat grains. RESULTS: A correlation-based feature selection (CFS) technique is also utilized to find the most effective among the 17 features. In the classification process using five selected features, the mean absolute error (MAE) and root mean square error (RMSE) values for ABC-based ANN are calculated as 0.00174 and 0.00433 respectively. The proposed technique is integrated into graphical user interface (GUI) software to construct an effective detection system for practical use. CONCLUSION: The results indicate that, thanks to the modified ANN algorithm and implemented CFS algorithm, the detection accuracy of damaged wheat grains is considerably increased. © 2019 Society of Chemical Industry.

Morphological traits of drought tolerant horse gram germplasm: classification through machine learning.

BACKGROUND: Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) is an underutilized pulse crop with good drought resistance traits. It is a rich source of protein. Conventional breeding methods for high yielding and abiotic stress tolerant germplasm are hampered by the scarcity of morphological data sets. Thus, horse gram cultivars considered for this study is classified based on prevailing growth factors showing homogenous genotype in various agro ecological zones. Nowadays, several machine learning (ML) methods are used in the field of plant phenotyping. RESULTS: We adopted unsupervised learning techniques from the K-means clustering algorithm to analyze important morphological traits: plant shoot length, total plant height, flowering percentage, number of pods per plant, pod length, number of seeds per plant, and seed length variants between germplasm. Unsupervised clustering revealed that 20 germplasm accessions were grouped in four clusters in which high-yielding traits were predominantly observed in cluster 2. CONCLUSION: These findings could guide ML-based classification to characterize suitable germplasms on the basis of high-yielding varieties for different agro-ecological zones. © 2020 Society of Chemical Industry.

A comparison of the nutritional value of Einkorn, Emmer, Khorasan and modern wheat: whole grains, processed in bread, and population-level intake implications.

BACKGROUND: Interest in alternatives to the traditional wheat Triticum aestivum among farmers, millers, bakers, and consumers is increasing. The Altergrain project aimed to compare the Belgian-soil cultivated Einkorn (1K), Emmer (EMM), Khorasan (KH), and modern wheat (MW) with respect to nutritional values of kernels, breads made from these cereals, and population-level nutrient intake implications. RESULTS: Ancient wheats 1K, EMM, and KH contain lower total carbohydrate content than MW. Further, ancient wheats are higher in both protein and crude ash content. Vitamin E levels in breads prepared using 1K and EMM were higher than those in MW, but those prepared from KH had lower vitamin E levels than MW. Breads prepared using ancient wheats have higher total phenol content (TPC) than those from MW. Baking caused a decrease in vitamin E and TPC in bread prepared from ancient wheat, the exception being the one prepared using KH, which had a higher TPC than MW. When replacing bread made from MW with those made from ancient grains, no differences were observed with respect to conformance with the Belgian Recommend Daily Requirements. CONCLUSION: Ancient wheats from Belgian soil are as nutritive as MWs even after being processed into bread. At the kernel level, nutritional differences are present, but only small differences are present in terms of nutritional intake when nutrition parameters are calculated for consumed bread. © 2020 Society of Chemical Industry.

Oil quality in sea-level quinoa as determined by cultivar-specific responses to temperature and radiation conditions.

BACKGROUND: There is renewed interest in quinoa as a potential source of vegetable oils; however, there is no information about how environmental conditions affect its fatty acid composition, a critical indicator of its oil quality. The fatty acid concentrations of four cultivars adapted to temperate environments were compared at three sowing dates to evaluate the effect of environmental conditions during the seed-filling period on the variation in oil quality. RESULTS: The interaction between cultivar and sowing date was the main source of variation explaining the changes in the lipid content and fatty acid concentrations in quinoa. Most of the variation in the concentration of unsaturated fatty acids was attributed to the temperature and solar radiation during the seed-filling period; cultivar-specific responses to photo-thermal conditions were observed among the sea-level quinoa cultivars evaluated. CONCLUSION: The lipid content and concentration of fatty acids in quinoa are affected by sowing date. This effect is exerted through changes in temperature and solar radiation conditions. This managing practice can therefore be used to achieve quinoa oil with different qualities. © 2019 Society of Chemical Industry.

Effects of exogenous amylase on the in vitro digestion kinetics of whole-crop maize silages made from flint or dent grain type at different phenological stages grown in tropical condition.

The effect of exogenous amylase on the in vitro rumen digestion kinetics of whole-crop maize silage made from dent (RB9004) or flint grain type (RB9308) was evaluated at different phenological stages: soft dough (SOD), early dent (EAD), ½ milkline (½M) and ¾ milkline (¾M). Forage was harvested from 70 to 110 days after sowing. Two rumen-cannulated cows receiving or not exogenous amylase (0.7 g/kg dry matter-DM, provided to achieve 396 kilo Novo units of amylase activity/kg of TMR DM) were used as donor of ruminal fluid. The in vitro gas production kinetics was evaluated according to a dual-pool logistic model. The chemical composition and gas production kinetics were affected by the hybrid and phenological stages. The flint hybrid had lower range for chemical analysis among physiological stages. Harvesting at ½M and ¾M improved DM content, bromatological composition and silage quality parameters compared to dent or flint types. Amylase (i) increased methane (CH4 ) production and in vitro dry matter digestibility (IVDMD) in ½M stage, (ii) improved digestion kinetics by reducing lag time and increasing total gas production and fermentation rates of non-fibrous carbohydrates (NFC) and fibrous carbohydrates (FC), and (iii) increased extent and fermentation rate of NFC and increased fermentation rate of FC fraction in whole-crop maize silages produced from dent or flint types in all phenological stages. Harvesting between ½M and ¾M is the best phenological stage to improve chemical composition and silage quality parameters. Exogenous amylase showed improvements on fibre digestion of silages at ½M and ¾M phenological stages in both grain types of corn.