Number Of Kernels Research Articles

Assessment of Genetic Variability, Heritability and Genetic Advance in Maize Genotypes (Zea mays L.)

Aim: The aim of the present research work was to estimate the genetic variability, broad sense heritability and genetic advance for various yield and its contributing characters of 39 maize genotypes.
 Methodology: 39 maize genotypes were sown in randomized block design and replicated thrice to evaluate various genetic parameters. The data was recorded on twelve quantitative traits including days to fifty percent tasseling, days to fifty percent silking, days to maturity, plant height, ear height, ear length, ear diameter, number of kernel rows/ear, number of kernels/row, 100kernels weight, shelling % and grain yield/plant.
 Results: The mean sum of squares due to genotypes for each of the 12 traits under study indicated significant differences in analysis of variance. High to moderate genotypic and phenotypic coefficients of variation as well as genetic advance were observed in the traits yield per plant, plant height, ear height, number of kernels per row, and 100-kernel weight. These findings suggest that the heritability of these traits is most likely the result of additive gene effects and that selection in early generations may be effective for these traits. On the other hand, the remaining characters days to 50% tasseling, days to 50% silking, number of kernel rows per ear, and shelling percentage were recorded with high heritability with low genotypic and phenotypic coefficient of variation and genetic advance suggesting non-additive gene action for control of these traits which provides limited scope for improvement by selection.
 Conclusion: Among the characters studied, grain yield, plant height, and ear height had high heritability and high values of genetic advance indicating that these characters were controlled by additive gene action and so phenotypic selection is effective for improvement of these traits.

Character Association and Path Coefficient Analysis to Determine Interrelationships among Grain Yield and its Components in Maize Genotypes (Zea mays L.)

Aim: A study was carried out to evaluate the magnitude of correlation between grain yield and its various contributing characters.
 Methodology: 39 genotypes of maize, comprising 31 hybrids and 8 parents, were assessed using randomized block design with three replications. The data recorded on twelve quantitative characters including days to 50 percent tasseling, days to 50 percent silking, days to maturity, plant height (cm), ear height (cm), ear length (cm), ear diameter (cm), number of kernel rows per ear, number of kernels per row, 100 kernel weight, shelling percentage and grain yield per plant.
 Results: The results of this study showed higher genotypic correlation coefficient for all the characters studied compared to the corresponding phenotypic correlation coefficient. This suggests that, despite the strong inherent association between the traits under study, environments’ influence masked its expression. Ear diameter was found to have a strong and significant positive correlation with grain yield per plant at phenotypic (0.9016) and (0.9540) genotypic level followed by ear length (0.8976, 0.9360), number of kernels per row (0.8905, 0.9247), plant height (0.8399, 0.8697), 100 seed weight (0.8070, 0.8544), ear height (0.7170, 0.7550) and number of kernel rows per ear (0.3240, 0.3901). Days to 50% silking and days to 50% tasselling, however, exhibited significant negative association with grain yield, while days to maturity showed non-significant negative. In path coefficient analysis the maximum positive direct effect on grain yield per plant was found by the ear diameter (0.3505), followed by the 100 seed weight (0.2362), ear length (0.2273), number of kernels per row (0.1827), days to 50% silking (0.1269), and shelling percentage (0.1223).
 Conclusion: The strong correlation of ear diameter, 100 seed weight, ear length and number of kernels per row (0.2971) with grain yield per plant appears to be mostly due to their high direct effects. Therefore, improving yield could be achieved through direct selection for these attributes.

An Investigation of Garlic’s Physical Properties Prior to Collection for the Development of a Garlic Collector

In this study, the physical properties of garlic just before collection were investigated, and garlic-collecting conditions are presented as basic data of design factors that can be utilized during the development of garlic collectors. The physical properties were subdivided into geometrical properties and mechanical properties, and the ecotype of garlic was divided into CSG (cold-season garlic) and WSG (warm-season garlic). Geometrical properties were compared between CSG and WSG by measuring the average weight, length, width, number of kernels, volume, and density of each cultivar, and WSG showed higher geometrical properties in all geometrical properties. In addition, there was garlic on the incomplete side of WSG, and shape non-uniformity was higher than that of CSG. There was no significant difference in the average Poisson’s ratio of mechanical properties. In the case of the modulus of elasticity, Namdo cultivars showed the lowest value at 15.52 MPa. The average allowable compressive stress measured using Hooke’s law was the lowest in the Uiseong cultivar (204.33 kPa) and the highest in the Danyang cultivar (376.42 kPa). The static friction coefficients among the four different materials by cultivar exhibited a commonality of being highest in aluminum and generally lowest in plastic. It is expected that the design of the garlic collector will be carried out in consideration of garlic when developing it using up-to-date physical properties of garlic just before collection.

Grain yield and its contributing traits in promising sweetcorn hybrids

AbstractThe augmentation of domestic sweetcorn (Zea mays L. saccharata) production in semiarid regions necessitates the development of new superior hybrids with high yield potential. This study aimed to identify the most influential characteristics contributing to sweetcorn grain yield in these regions. Nineteen promising sweetcorn hybrids and the commercial hybrid KSC403su as the control were examined using a randomized complete block design with three replications in a 2‐year study (2021–2022) at the Khorasan Razavi Agricultural and Natural Resources Research and Education Institute Center (ARREO) in Mashhad, Iran. The combined analysis of variance revealed significant variances attributed to genotype (G) for all studied traits, while year (Y) and G × Y effects were significant for most characteristics. Phenotypic correlations indicated a highly significant association of grain yield with grain depth (r = 0.79, p < 0.01), kernel number per row (r = 0.75, p < 0.01), row number per ear (r = 0.63, p < 0.01), ear length (r = 0.58, p < 0.01), and ear diameter (r = 0.57, p < 0.01) traits. Path analysis results emphasized the critical role of traits such as grain depth, row number per ear, and ear length, showcasing a highly significant positive direct impact on grain yield and also a significantly positive association with grain yield. In this study, hybrids PSM3, PSM4, PSM9, PSM16, and PSM19 exhibited the highest values for grain yield and yield‐contributing traits, suggesting their potential for further research in multiple locations with varying environmental conditions before being recommended to farmers.

Genetic gains in short‐season corn hybrids: Grain yield, yield components, and grain quality traits

AbstractEra studies are important to understand historical changes in maize (Zea mays) germplasm and estimate genetic gains, yet information for short‐season maize hybrids is limited. Here, we determine grain yield genetic gain in Bayer short‐season hybrids (100–105 days) and investigate indirect changes made on 17 secondary traits, including yield components (kernel number, weight, and shelling efficiency), and grain quality traits (oil, protein, starch, ethanol, moisture, and test weight). We evaluated 40 maize hybrids released from 1980 to 2020 across 18 environments in the US Corn Belt. Plant density and N‐fertilizer were held constant within each environment. Results indicated a linear increase in grain yield (from 11.1 to 15.3 Mg ha−1, 105 kg ha−1 year−1, or 0.8% year−1) with no sign of a plateau. The increase in grain yield was attributed more to increased kernels per m2 (0.57% year−1) than kernel weight (0.23% year−1). Grain protein concentration decreased until the late 2000s and plateaued thereafter, while starch and ethanol concentration increased until the early 2000s and plateaued thereafter. However, the total amount of protein, starch, and ethanol increased linearly from 1980 to 2020. We concluded that maize breeding for increased grain yield has indirectly affected many traits at different rates and directions. Our results are encouraging for future progress in grain yield increase, update genetic gain information to 2020 for short‐season hybrids, and can inform plant breeders, crop physiologists, agronomists, and crop modelers.

Effect of 2, 4-D sodium salt on weeds, growth and yields in rabi maize (Zea mays L.)

A field investigation was conducted during winter (rabi) season of 2021 at Agricultural Research Farm of Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, to study the efficacy of doses of 2, 4-D sodium salt 80% WP on weeds, productivity and economics of rabi maize (Zea mays L.). Application of 2, 4- D sodium salt 80% WP @ 1,250 g/ha at 25 days after sowing (DAS) resulted in to lesser weed density and weed dry weight of Physalis minima Roem. & Schult, Chenopodium album (L.), Melilotus indicus (L.) and Cichorium intybus (L.) as compared to 2, 4-D sodium salt 80% WP @ 1,000 g/ha and 2, 4-D sodium salt 80% WP @ 750 g/ha at 60 DAS, with higher weed control efficiency (77.3 %) and lesser weed index (34.9 %). At 60 DAS, 2, 4-D sodium salt 80% WP (applied @1250 g/ha at 25 DAS) produced higher plant height, number of leaves, chlorophyll content, leaf area index and plant dry-matter over other treatments except weed free and two hand weeding at 20 and 40 DAS. Application of 2, 4-D sodium salt 80% WP @ 1,250 g/ha at 25 DAS recorded higher number of cobs, number of kernels, number of kernels/row and seed index as compared to 2, 4-D sodium salt 80% WP @ 1,000 g/ha, and 2, 4-D sodium salt 80% WP @ 750 g/ha. Higher kernel (3.7 t/ha) and stover yield (4.7 t/ha) of maize was recorded due to application of 2, 4-D sodium salt 80% WP @ 1,250 g/ha at 25 DAS with higher harvest index (44.5%) and monetary returns (62.5 × 103 `/ha) as compared to 2, 4-D sodium salt 80% WP @ 1,000 g/ha, and 2, 4-D sodium salt 80% WP @ 750 g/ha.

Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI.

Rapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers. The feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella typhimurium (ST) contaminated on mutton was explored. The hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (108, 107, 106, 105, 104, 103 and 102 CFU/mL) of EC, SA and ST were acquired. The one dimensional convolutional neural network (1D-CNN) model was constructed and the influence of structure hyperparameters on the model was explored. The effects of different spectral preprocessing methods on partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and 1D-CNN models were discussed. In addition, the feasibility of using the characteristic wavelength to establish simplified models was explored. The best full band model was the 1D-CNN model with the convolution kernels number of (64, 16) and the activation function of tanh established by the original spectra, and its accuracy of training set, test set and external validation set were 100.00, 92.86 and 97.62%, respectively. The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32, 16) and the activation function of tanh were 100.00%. In addition, the accuracies of all simplified models were 100.00% except for the 1D-CNN models. Considering the complexity of features and model calculation, the 1D-CNN models established by original spectra were the optimal models for pathogenic bacteria contamination status and species. The simplified models provide basis for developing multispectral detection instruments. The results proved that SWIR-HSI combined with machine learning and deep learning could accurately detect the foodborne pathogen contamination on mutton, and the performance of deep learning models were better than that of machine learning. This study can promote the application of HSI technology in the detection of foodborne pathogens on meat.

Effects of Tillage Practices on the Growth and Yield of Maize, in the Guinea Savannah Ecological Zone of Ghana

Abstract: A field study was conducted to investigate the influence of conventional tillage (CVT) and conservation tillage (CST) on various aspects of maize yield parameters and grain yield, using the Wagdaata maize variety. The study was conducted at the experimental farm of Tamale Technical University, located at Northern region, Tamale, Sagnarigu municipality in the Guinea savannah ecological zone of Ghana in 2023 cropping season. A randomised complete block design was used with three replicates. The treatments consisted of two tillage practices: conventional tillage (CVT) and conservation tillage (CST) and a control. The CVT involved plowing the soil to a depth of 15cm using a tractor plough with disk to plow. The CST involved use of hoe to make ridges and the control was direct seeding without loosen the soil. Plant height was evaluated every two weeks by measuring the height of maize to the base of the apex leaf. Other data collected were: maize leaf area. Cob characteristics including ear height, cob weight with husk, fresh cob weight, and dry cob weight, number of cobs per hectare and number of kernels per cob, 1000 seed weight and grain yields. The present study revealed that plant height, maize leaf area and shoot dry weight increased in the ridges than the other tillage practices. Cob characteristics such as ear height, cob weight with husk, fresh cob weight, and dry cob weight, number of cobs per hectare and number of kernels per cob, 1000 seed weight and grain yields was also optimum with the ridges as a against the other tillage practices. The study showed increased grain yield by 66% in the ridge tillage (2,389 kg/ha) over the plough tillage (1,436 kg/ha). The study provides comprehensive insights into the impact of tillage practices on various aspects of maize growth and yield, with ridge tillage emerging as a favorable practice for optimizing maize plant development and overall productivity in the Guinea savannah ecological zone of Ghana.

Vegetative and Reproductive Responses Induced by Organo-Mineral Fertilizers on Young Trees of Almond cv. Tuono Grown in a Medium-High Density Plantation

Field experiments were conducted in three successive seasons (2019–2021) to evaluate the effects of four commercial organo-mineral fertilizers with biostimulating action (Hendophyt®, Ergostim®, and Radicon®) on the vegetative and productive performance of young almond trees (Prunus dulcis, cv. Tuono) grown in a semiarid climate in Southern Italy. Foliar treatments were applied three times during each season (at the swollen bud, beginning of flowering, and fruit set-beginning of fruit growth stages). Both 2020 and 2021 were adversely affected by late frosts, resulting in damage to the flowers and small fruits without any positive effect of the biostimulant applications. In contrast, the results obtained during the normal climate year (2019) indicated that the growth of trunk diameter and shoot length of trees tended to increase in biostimulant treatments compared to those of the control. The number of buds and flowers per unit length of the branch revealed no significant differences among years and all compared treatments. However, in 2019, the fruit set percentage, number, and weight of kernels per tree were significantly higher in the biostimulant treatments compared to those of the control. To this regard, the use of biofertilizers is suitable for maintaining soil fertility and improving crop productivity This information holds significance for almond tree growers.

A Novel Single-Word Speech Recognition on Embedded Systems Using a Convolution Neuron Network with Improved Out-of-Distribution Detection

Advancements in AI have elevated speech recognition, with convolutional neural networks (CNNs) proving effective in processing spectrogram-transformed speech signals. CNNs, with lower parameters and higher accuracy compared to traditional models, are particularly efficient for deployment on storage-limited embedded devices. Artificial neural networks excel in predicting inputs within their expected output range but struggle with anomalies. This is usually harmful to a speech recognition system. In this paper, the neural network classifier for speech recognition is trained with a “negative branch” method, incorporating directional regularization with out-of-distribution training data, allowing it to maintain a high confidence score to the input within distribution while expressing a low confidence score to the anomaly input. It can enhance the performance of anomaly detection of the classifier, addressing issues like misclassifying the speech command that is out of the distribution. The result of the experiment suggests that the accuracy of the CNN model will not be affected by the regularization of the “negative branch”, and the performance of abnormal detection will be improved as the number of kernels of the convolutional layer increases.

RECURRENT SELECTION FOR GENERAL AND SPECIFIC COMBINING ABILITY IN MAIZE

This study was aimed to identify efficiency of recurrent selection program to improve maize population. This program was applied to var. Buhooth 106 with var. 5012 Using RGCA with the inbred Zm7 for the production of RSCA. The recurrent selection program was implemented for four consecutive seasons (2020 - 2021) at the Fields Station of the College of Agriculture - University of Anbar. In the spring season 2020, 100 top crosses were obtained from both varieties and progeny. During the fall season 2020, an experiment was carried out to compare the top crosses of each variety RGCA and RSCA using a 10 × 10 partially balanced lattice design.. Six genotypes from each genetic population as well as Buhooth 106 and 5012 and Zm7 inbreds). The genotype-S1-2 RSCA achieved the lowest female flowering period for females 61.50 days, the highest number of rows per ear 17.80 rows, and the highest grain yield 177.82 g. The genotype-S1-3 RSCA achieved the highest leaf area 0.593 m2 and the highest number of kernels per row 43.11 kernel. The broad sense heritability of the traits of the number of rows and the number of kernels in the row and the yield of the plant reached 53.23%, 73.85% and 41.94%, respectively, and high genetic advance for kernels per row and grain yield of 4.79 and 15.52 respectively.

MA-CNN based spindle thermal error modeling using the depth feature analysis with thermal error mechanism

The dynamic changes in the temperature fields make it unavoidable for the spindle to undergo thermal deformation. Thermal error modelling is helpful in enhancing the accuracy of machine tools. This paper proposes a spindle thermal error modelling method based on the mayfly algorithm (MA) for complex working conditions. First, different working conditions are set to obtain thermal deformation and temperature data. A thermal error model is established using MA. MA is used to optimize the number and size of convolutional kernels. Next, the depth features of the model are compared with the exponential thermal error model in logarithmic space. The model is compared with five other models. The results indicate that the MA-CNN model has a higher accuracy. Finally, the compensation is conducted in three directions. The results show that the average thermal error in the Z direction is reduced by 63.75 %, which demonstrates the good performance of the model.

Yield related traits in some Persian walnut cultivars: Analysis of genetic and genetic by environment interaction

The most important trait in tree species, including walnut, is the yield. In this study, the effect of genotype and their interaction with year on Nut weight, Kernel weight, Kernel percentage, Fruit set, Nuts number on Scaffold (Canopy) Cross Area (SCA), Nut weight on SCA and Kernel weight on SCA were evaluated on Caspian, Persia, Alvand, and Chaldoran walnut cultivars. The results showed that the effects of year, genotype, and year × genotype interaction on all traits were significant. The results showed that Alvand had the highest number of nuts (41.8 per m2) and nut weight (472.1 g/m2) on (SCA). Heritability (H2b) for kernel weight and kernel percentage, were estimated 0.75 and 0.80, respectively. The lowest value of H2b (0.36) was belong to fruit set. The analyses of genetic and phenotypic correlations between traits showed that, the nut weight had (rg = 0.31, rp = 0.27) a moderate correlation with SCA same as kernel weight (rg = 0.34, rp = 0.29). The GGE biplot analysis explained most of the existing variations (>90%). The genetic effect (PC1) for all traits were higher respect to the genetic × environment interaction (PC2), especially for the kernel percentage (94.4%) and number and weight of nut and kernel on SCA (>90%). The lowest value of the PC1 was related to the fruit set (65.6%), which indicates the trait was more affected by genetic × environment interactions (21.8%). So, this result showed that the yield-related traits in walnut is highly relevant to environment(year in this study) and evaluation of the new cultivars needs careful attention in this case.

Effect of salinity and drip fertigation on maize (<i>Zea mays</i>) and water use efficiency

Climatic variability is immensely pressurised on freshwater availability in arid and semi-arid ecosystems, leading to poor quality water for crop production not becoming uncommon, especially in coastal ecosystems. A field experiment was conducted during winter (rabi) season of 2022–23 at the Saline Water Scheme at Agricultural College Farm, Acharya N. G. Ranga Agricultural University, Bapatla, Andhra Pradesh to assess the impact of saline water on maize growth, yield and water use efficiency under drip fertigation. The experiment consisted 8 treatments laid in randomized block design (RBD) with 4 replications. The utilization of the BAW irrigation method, together with the application of a prescribed quantity of fertilizer, resulted in the highest plant height and dry matter accumulation during all phases of crop growth. These outcomes were comparable to those achieved by using a combination of fresh water and saline water with an electrical conductivity of 2 dS/m. The maximum values for the number of kernels per cob (451), cob length (17.2 cm), cob weight (164.4 g), kernel yield (7.38 t/ha), stover yield (9.61 t/ha) and water use efficiency (18.5 kg/ha mm) of maize were observed under the irrigation treatment T4. The parameters exhibited their minimum values when irrigation was conducted using water with a salt level of 4 dS/m and no fertilizers were used. However, there was no statistically significant difference observed in the number of cobs per plant and harvest index of maize when saline water was used in conjunction with drip fertigation. In the context of maize cultivation, employing a cyclic irrigation technique that alternates between salty (EC 2–4 dS/m) and freshwater sources, in conjunction with recommended dose of fertiliser (RDF) has the potential to deliver comparable crop development, productivity, and economic benefits when compared to the use of freshwater sources alone.

ECG-based emotion recognition using random convolutional kernel method

The recognition of human emotions based on physiological signals has garnered significant attention from various fields such as human–computer interaction, cognitive-behavioral science, and the treatment of emotion-related diseases. Using the easily accessible ECG signal for emotion recognition presents a viable approach. However, method relying on hand-crafted features necessitate task-specific domain knowledge and consume substantial time to design suitable features, and studies employing deep learning technologies incur greater computational complexity and extended training durations. To address these problems, we proposed an emotion recognition method that employs random convolutional kernels on ECG signals. Following ECG signal preprocessing, a large number of random convolutional kernels were used to transform the signals, and the resulting features are utilized to classify the various emotion states. In addition to accurately and automatically extracting the multi-scale features from ECG signals, combining the random convolutional kernels with ECG signal alone also reduces the computational complexity and training time compared to methods using multiple physiological signals or deep neural networks. Validation on three publicly available datasets revealed that the proposed method achieved recognition accuracies of 91.5%, 96% and 92.5% for valence, arousal and dominance in AMIGOS dataset, and 95.1%, 98.8% and 90.2% for same labels in DREAMER dataset. The accuracy for valence, arousal and affective states in WESAD dataset are 94.5%, 91.8% and 91.8%. The results show that the proposed method outperforms previous researches in multi-class emotion recognition, thus offering a new and promising approach for swift and precise emotion recognition.

Genetic Variability for the Yield and Yield-Related Traits in Some Maize (Zea mays L.) Inbred Lines in the Central Highland of Ethiopia

After rice and wheat, maize (Zea mays L.) is the third most significant and valuable crop in terms of industrial production in the world. The experiment was conducted to determine the nature and magnitude of genetic variability for various traits of maize inbred lines at the Ambo Plant Protection Research Center experimental field during 2013/2014, the major cropping season. The experiment’s design was described in 5 × 5 triple lattice designs. The results of the analysis of variance revealed that genotypic mean squares were significant for all traits, indicating that the inbred lines under study had a higher level of genetic diversity. In terms of genotypic and phenotypic variation, the largest coefficient was found in grain yield per hectare (28.49 and 35.43) followed by the number of tassel branches (23.14 and 24.92), respectively. Both days to 50% silking (91.57) and days to 50% tasseling (86.56) showed a high magnitude of broad-sense heritability. The results of the phenotypic and genotypic correlation analyses showed that grain yield per plant significantly correlated positively with five traits at both the phenotypic and genotypic levels. The number of kernels per row, aboveground biomass production per plant, harvest index, and grain yield per hectare might all be employed as selection criteria to increase the maize grain yield, according to path coefficient analysis at the phenotypic and genotypic levels. The grain yield and other significant yield components were found to be superior with inbred lines AMH169-55 and AMH169-86. Therefore, it is advised that these lines be used to further enhance the maize crop.

Agro-morphological characterization of Pakistani maize accessions using qualitative and quantitative traits.

Agro-morphological characterizations offer robust and vigorous means for the precise characterization of germplasm to be used in breeding programs. Here, agro-morphological features-based analyses were considered to figure out the genetic variability within 99 maize accessions and five (5) check varieties including Azam, W. Islamabad, Haq Nawaz, Ev-5 and Evr197. A total of 28 important agro-morphological traits were accounted in the field trails at Hazara University Mansehra Pakistan, during spring 2015. The maximum variation was detected in grain weight per cob (53.43), followed by number of kernels per row (38.64) and flag leaf area (cm2), (38.09%). Cluster analysis divided the 99 maize accession with five check varieties of maize accessions into 7 clusters following the hierarchical clustering. Cluster II recorded 29 genotypes with maximum grain yield per cob, and highest flag leaf length, followed by cluster IV. Lowest grain yield per cob was noted for accessions of cluster I. Similarly, cluster VII consisted of accession with the longest cob length. Moreover, the investigations also revealed that the primary constituent among first five principal components with an eigen value about more than 0.98 in relation to 68.75% of the total variants. PCI accounted for 25.53%, PCII contributed 18.31%, and PCIII is 9.88% of the overall morphological variability were significant contributors were grain weight per cob, number of kernel per row, 1000 grain weight. The identification of a significant level of genetic diversity during the present investigation having implications for maize germplasm characterisation, conservation, and breeding programs aiming at developing improvement maize cultivars.

Influence of climatic variables on maize grain yield and its components by adjusting the sowing date.

Yield and its components are greatly affected by climate change. Adjusting the sowing date is an effective way to alleviate adverse effects and adapt to climate change. Aiming to determine the optimal sowing date of summer maize and clarify the contribution of climatic variables to grain yield and its components, a consecutive 4-year field experiment was conducted from 2016 to 2019 with four sowing dates at 10-day intervals from 5 June to 5 July. Analysis of historical meteorological data showed that more solar radiation (SR) was distributed from early June to mid-August, and the maximum temperature (Tmax) > 32°C appeared from early July to late August, which advanced and lasted longer in 1991-2020 relative to 1981-1990. Additionally, the precipitation was mainly distributed from early June to late July. The climate change in the growing season of summer maize resulted in optimal sowing dates ranging from 5 June to 15 June, with higher yields and yield stability, mainly because of the higher kernel number per ear and 1,000-grain weight. The average contribution of kernel number per ear to grain yield was 58.7%, higher than that of 1,000-grain weight (41.3%). Variance partitioning analysis showed that SR in 15 days pre-silking to 15 days post-silking (SS) and silking to harvest (SH) stages significantly contributed to grain yield by 63.1% and 86.4%. The extreme growing degree days (EDD) > 32°C, SR, precipitation, and diurnal temperature range (DTR) contributed 20.6%, 22.9%, 14.5%, and 42.0% to kernel number per ear in the SS stage, respectively. Therefore, we concluded that the early sowing dates could gain high yield and yield stability due to the higher SR in the growing season. Meanwhile, due to the decreasing trend in SR and increasing Tmax trend in this region, in the future, new maize varieties with high-temperature resistance, high light efficiency, shade tolerance, and medium-season traits need to be bred to adapt to climate change and increased grain yield.

Optimizing planting density and nitrogen application to mitigate yield loss and improve grain quality of late-sown wheat under rice-wheat rotation

Late sowing is a critical factor that hinders achieving high-yield, good-quality wheat under rice-wheat rotation. Understanding the physiological basis and regulatory pathways that lead to late-sown wheat of high yield and good quality is crucial for developing effective cultivation strategies. A 2-year field experiment was conducted to investigate the effects of sowing date, nitrogen (N) application rate, and planting density on wheat yield, grain quality, population characteristics, and the underlying physiological factors. The results revealed significant interactions among the sowing date, planting density, and N application in regulating both yield and quality. Late sowing reduced grain yield primarily by reducing the number of spikes and kernels. However, the latter was improved by increasing N application and the planting density, thus mitigating the yield losses caused by late sowing. Moreover, the grain protein content (GPC) and wet gluten content (WGC) increased with delayed sowing dates and higher N rates, but decreased with increased planting densities. For wheat yields over 9,000 or 7,500 kg ha–1, the latest sowing date should not be later than Nov. 4 or 15, respectively. In addition, specific criteria should be met, including a maximum of 1.5 and 1.0 million stems and tillers ha–1, a maximum leaf area index of 6.7 and 5.5, and a dry matter accumulation (DMA) at anthesis of 14,000 and 12,000 kg ha–1, respectively. For high-yield, good-quality late-sown wheat, the optimal combination is a 25% increase in the N rate (300 kg N ha–1) and a planting density of 2.25 million (N300D225) or 3.75 million (N300D375) plants ha–1 for 10- or 20-day delays in sowing, respectively. These combinations result in higher leaf net photosynthetic rate; higher activities of leaf nitrate reductase, glutamine synthetase, and grain glutamic pyruvic transaminase; and a lower sugar-N ratio during post-anthesis.

A Method for Detecting the Death State of Caged Broilers Based on Improved Yolov5

HighlightsRapid and accurate identification and detection of mortality in high-level caged broilers.Apply the state-of-the-art single-stage object detection model Yolov5 as the experimental infrastructure.Modify and improve the network structure, reduce the calculation amount of model parameters, and make the model easier to deploy.The data of dead broilers in different regions can also be quickly and accurately identified and detected, with excellent generalization.Abstract. In an unmanned broiler base, the automatic identification of the death state of broilers in a complex cage environment can effectively improve picking efficiency and reduce its negative impact. An improved Yolov5 object detection method is proposed in this study to rapidly and accurately detect the presence of dead broilers in caged structures. The Yolov5 model within the CSPDarknet53 framework is constructed, and the Yolov5 model is experimentally pruned to reduce the number of convolution kernels. The Ghost structure and the Involution structure are combined with the Yolov5 backbone network to effectively reduce the number of parameters and computations with a negligible loss of accuracy, making it easier to deploy applications. The detection accuracy of the model can reach 99.2%. The number of parameters is reduced from 7.05M to 0.31M, and the amount of calculation is reduced from 16.3 to 0.3. It can be seen that the improved Yolov5 network is practical and suitable for high-precision real-time detection and recognition of the death state of broilers in a caged structure. Keywords: Keywords.,Computer vision, Dead chicken detection, Machine vision, Yolov5.