Tasseling Stage Research Articles

Optimizing Nitrogen Input Increased Yield and Efficiency in Maize-Soybean Strip Intercropping System

Optimizing nitrogen (N) fertilizer combination is a crucial measure to maximize yield and production efficiency in a maize-soybean strip intercropping system (MSSI). In this field experiment, six maize/soybean N input combinations (0 kg ha−1, F0; 255/30 kg ha−1, F1; 255/45 kg ha−1, F2; 255/60 kg ha−1, F3; 210/45 kg ha−1, F4; 300/45 kg ha−1, F5) were set in 2022 and 2023. The results indicated that optimizing N combination (maize/soybean, 255/45 kg ha−1) could synergistically increase yield and economic benefits. Path analysis results showed that the grain numbers in maize and soybean emerged as the most critical yield-affecting factors. Compared with F0, F5 showed the highest grain yield during the 2-year experiment, and the net return increased by 86.1% (F1), 133.3% (F2), 87.4% (F3), 104.7% (F4), and 128.3% (F5), respectively. Optimizing N input under F2 and F5 notably enhanced the leaf area index (LAI) of maize at the milk stage (R3) and soybean at the full pod stage (R4). Additionally, optimization of N distribution in maize stems at the tassel stage (VT) and soybean leaves at the initial flowering stage (R1) facilitated increased dry matter and N accumulation at the maturity stage, resulting in final land equivalent ratios (LER) of 1.44 and 1.55, respectively. Our results provide a more valuable field N combination for summer maize planting areas (sandy soil areas) in Huang-Huai-Hai and southern China, thus promoting the wider application of MSSI.

Impacts of nitrogen (N), phosphorus (P), and potassium (K) fertilizers on maize yields, nutrient use efficiency, and soil nutrient balance: Insights from a long-term diverse NPK omission experiment in the North China Plain

Context or problemSoil nutrient deficiency is one of the significant challenges in grain production, particularly nitrogen (N), phosphorus (P), and potassium (K). These deficiencies not only reduce crop yields but also cause associated environmental issues, such as soil structure deterioration and ecosystem services diminution. ObjectivesThis research aimed to investigate the long-term effects of NPK fertilizers on soil nutrient properties and maize phenology, further on the grain yield, and to evaluate the nutrient use efficiency and soil nutrient balance under different fertilization managements. MethodsA long-term field experiment was initiated in 1990 in a summer maize field in the North China Plain, including five fertilizer treatments: CK (control), NP, NK, PK, and NPK. The soil nutrient properties, maize yields, crop nutrient uptake amount, nutrient recovery efficiency (NRE), nutrient harvest index (NHI), and soil nutrient balance were annually evaluated from 2005 to 2022. ResultsSignificant improvements in maize yields were found under NPK (9081 kg ha−1), NP (6426 kg ha−1), and PK (2668 kg ha−1) compared with CK (1809 kg ha−1) and NK (1656 kg ha−1). The yield increase was mainly attributed to: (1) enhancing in soil nutrient properties, such as soil organic carbon, soil total N (TN), available N (AN), total P (TP), available P (AP), and available K (AK), and (2) the shortened vegetative period, leading to greater sunshine hours (SH) and accumulative growing degree days (GDD) during the reproductive period. Furthermore, a random forest analysis quantified their importance to grain yield, showing that the edaphic factors (mainly SOC, TN, AK, AN, TP, AP, C:N, and N:P) explained a much greater proportion of yield variation compared with phenological factors (mainly GDD during tasseling and physiological maturity stages, and SH during tasseling stage). Additionally, the significantly higher response ratio of both N and P to NRE and NHI implied that N and P fertilizers having a more pronounced impact on improving nutrient use efficiency than K fertilizer. In terms of soil nutrient balance, a most relative soil nutrient balance was detected under NPK treatment, avoiding either substantial nutrient depletion or accumulation under any nutrient deficiency conditions. ConclusionsSoil deficiencies in N and P had more severe impacts on maize yields and nutrient use efficiency compared with K deficiency. Additionally, a balanced NPK fertilizer regime effectively managed soil nutrient balance. Implications or significanceThese findings elucidate the roles of N, P, and K fertilizers in maize production and soil nutrient conditions from a long-term field experiment, which could provide valuable insights for optimizing fertilization management strategies.

Evaluation of functional plant growth-promoting activities of culturable rhizobacteria associated to tunicate maize (Zea mays var. tunicata A. St. Hil), a Mexican exotic landrace grown in traditional agroecosystems.

Tunicate maize (Zea mays var. tunicata A. St. Hil) is a landrace that constitutes a fundamental aspect of the socio-cultural identity of Ixtenco, Tlaxcala (Mexico) and represents an exotic phenotype whose kernels are enclosed in leaflike glumes. Despite multiple studies conducted worldwide on plant growth-promoting-rhizobacteria (PGPR) in commercial maize varieties grown under monoculture systems, very little is known about bacteria inhabiting native maize landraces in agroecosystems, but for tunicate maize such knowledge is non-existent. This research described and profiled functional groups of culturable rhizobacteria from tunicate maize at two phenological stages (tasseling and maturity/senescence) in a polyculture system, highlighting potential PGPR for biotechnological purposes. Ninety-five rhizobacteria were isolated and molecularly identified, and their physiological activities such as plant growth promotion, production of exogenous lytic enzymes, and antagonism against fungal pathogens were determined. The culturable rhizobacterial community associated to tunicate maize comprised 42 genera, dominated by Bacillaceae, Comamonadaceae, Microbacteriaceae, Micrococcaceae, Oxalobacteraceae, Pseudomonadaceae, and Rhizobaceae families. At tasseling stage, the identified bacteria corresponded to Arthrobacter, Priestia, Herbaspirillum, Pseudomonas, and Rhizobium, and exhibited redundant capabilities for stimulating plant growth and nutrition, and inhibiting fungal phytopathogens. At maturity/senescence stage, the main genera Arthrobacter and Microbacterium displayed lytic capabilities to support mineralization process. We recorded potential novel rhizosphere functional bacteria such as Rhizobium, Sphingobium, and Arthrobacter which are not previously described associated to maize landraces, as well as their bioprospection as PGPR detected at plant phenological stages poorly explored (like maturity/senescence). This taxonomic and functional diversity was attributed to the application of agricultural practices as well as the rhizosphere effect during specific phenological stages. Results described the diversity and functionality of culturable rhizosphere bacteria from tunicate maize in polyculture systems that allowed us the detection of potential rhizobacteria for further developing of biofertilizers and biocontrollers directed as biotechnology for sustainable agriculture, and for generating strategies for conservation of native plants and their microbial genetic resources.

Unraveling the Relationship between Soil Nutrients and Maize Leaf Disease Occurrences in Mopani District Municipality, Limpopo Province, South Africa

Maize is a staple crop important for food security that millions globally depend upon as an energy source, primarily due to its high starch and fat content. For growth and disease resistance, maize production requires a balanced intake of essential nutrients, including nitrogen (N), phosphorus (P) and potassium (K). This study investigated the relationship between soil nutrient levels and maize disease occurrences in the Mopani District Municipality, Limpopo Province, South Africa. Soil and maize leaves were collected using a systematic sampling approach. Grids of 10 × 10 m were created, covering a maize field. Forty soil samples were collected a day before the planting date and sent to the laboratory for analysis of N, P and K. During the tasseling stage of the maize plant, 40 maize leaf samples were collected and sent to the laboratory for disease identification. Maize leaves were classified as healthy, southern corn leaf blight (Bipolaris maydis), northern corn leaf blight (Exserohilum turcicum), maize streak disease (Maize streak virus), nitrogen-deficient or phosphorus-deficient. Generalized Linear Models (GLMs) with a corrected Akaike Information Criterion (AICc) showed a significant relationship between low soil nutrient levels of N, P and K and maize disease occurrence (p < 0.0001). The interaction of the N*P*K model had the lowest AIC value (AICc = 28.53), indicating the necessity of considering synergistic effects in maize disease management. All the model performances had a delta AICc = 0. These findings highlight the significance of comprehensive soil management strategies in enhancing the disease resistance, well-being and yields of maize crops.

Extraction of Maize Distribution Information Based on Critical Fertility Periods and Active–Passive Remote Sensing

This study proposes a new method for integrating active and passive remote sensing data during critical reproductive periods in order to extract maize areas early and to address the problem of low accuracy in the classification of maize-growing areas affected by climate change. Focusing on Jiaozuo City, this study utilized active–passive remote sensing images to determine the optimal time for maize identification. The relative importance of features was assessed using a feature selection method combined with a machine learning algorithm, the impact of both single-source and multi-source features on accuracy was analyzed to generate the optimal feature subset, and the classification accuracies of different machine learning classification methods for maize at the tasseling stage were compared. Ultimately, this study identified the most effective remote sensing features and methods for maize detection during the optimal fertility period. The experimental results show that the feature set optimized for the tasseling stage significantly enhanced maize recognition accuracy. Specifically, the random forest (RF) method, when applied to the multi-source data fusion feature set, yielded the highest accuracy, improving classification accuracy by 24.6% and 4.86% over single-source features, and achieving an overall accuracy of 93.38% with a Kappa coefficient of 0.91. Data on the study area’s maize area were also extracted for the years 2018–2022, with accuracy values of 93.83%, 98.77%, 97%, and 98.05%, respectively.

Growth Parameters and Protein Content of Maize as Influenced by Sowing Methods and Different Levels of Nitrogen

The field experiment was carried out at Research Farm, Integral University, Lucknow, Uttar Pradesh, India during Kharif season, 2021. The experiment envisages to study the effect of Sowing methods and different Nitrogen levels on yield and attributes of maize crop. The experiment was laid down in the Split Plot Design and the treatments were replicated three times. The maximum plant height of 56.92 cm, 235.17 cm and 237.16 cm was observed in the ridge method of sowing at knee height stage, tasselling stage and at harvest stage, respectively. Among nitrogen levels, the maximum plant height of 57.61 cm, 235.29 cm and 238.38 cm was observed at knee height stage, tasselling stage and at harvest stage, respectively in the treatment (N5) 25% N through granular urea + 75% N through Nano-urea. Among nitrogen levels, the maximum DMA of 86.30, 425.36 and 1287.73 g sqm-1 day-1 was observed at knee height stage, tasselling stage and at harvest stage, respectively in the treatment (N5) 25% N through granular urea + 75% N through Nano-urea. At harvest stage number of leaves per plant were highest in the S1 (14.33) and N5 (16.13) treatment. Among nitrogen levels, the maximum leaf area index (LAI) of 1.55, 2.49 and 1.9 was observed at knee height stage, tasselling stage and at harvest stage, respectively in the treatment N5. Among sowing methods, highest protein content was found in the ridge method (10.14%) followed by flat method (9.81%) and broadcast method (9.42%) of sowing. The protein yielded maximum of 586.15 kg ha-1 in the N5 treatment. All parameters showed better in granular urea application rather than nano-urea, when used solely.

Response of Maize (<i>Zea may</i> <I>L.</I>) Cultivars to Urea Time Application on Phenology, Growth and Grain Yield at Bako, East Wollaga, Ethiopia

The results of this research provide a basis for additional exploration and advancement, which will culminate in suggestions for the timing of split urea treatment and cultivars for sustainable maize production within the study region. During the 2019 and 2020 growing seasons, a field experiment was conducted at the Bako Agricultural Research Center to determine the response of hybrid maize cultivars to urea split application on phenology, growth, and grain production. In the experiment, six levels of urea split application were applied to two types of corn. T1 = 1/2 dose of urea at planting + 1/2 dose of urea at Knee height; T2 = 1/2 dose of urea at planting + 1/2 dose of urea at tasseling; T3 = 1/ 3 dose of urea at planting + 1/3 dose of urea at knee height + 1/3 dose of urea at tasseling; T4 = 2/3 dose of urea at knee height + 1/3 dose of urea at tasseling; T5 = 1/2 dose of urea at knee height + 1/2 dose of urea at tasseling; and T6 = 1/4 dose of urea at planting + 1/2 dose of urea at knee height + 1/4 dose of urea at tasseling. The experiment's randomized complete block design made use of three replications. The primary effects of the urea split time of application were found to have a substantial impact on plant height, 90% physiological maturity, grain production, total leaf area per plant, and leave area index. Three urea split applications 1/4 dosage at planting, 1/2 dose at knee height, and 1/4 dose at tasseling provided the maximum net benefit of EB 246,536.7 ha-1 and the best marginal rate return of 2822.26% for maize output. In conclusion, urea split application 1/4 doses at planting stage 1/2 dose at knee-height and 1/4 dose at tasseling stage is the best time of application in good rainy seasons and hence recommended for the end users. However, in the case of erratic and heavy rainy seasons, application at three times should be used to get maximum profit and acceptable MRR.

Maize seed inoculated with Azospirillum brasilense as strategy to improve photosynthetic nitrogen use efficiency in the Amazonian periphery

In the Amazonian region of Brazil, the combination of different inoculation forms of Azospirillum brasilense and N rates has not been studied for its effect on photosynthetic N use efficiency and yield in maize plants. The effects of inoculation forms of A. brasilense and N rates on photosynthetic N use efficiency and maize yield were evaluated. Two field trials were conducted, combining four N rates (0 to 180 kg ha−1) with different inoculation forms of A. brasilense (seed, leaf, or control). Photosynthetic N use efficiency (photosynthesis per unit leaf N) was taken at the beginning of tasseling stage, while yield and N use efficiency components were evaluated at physiological maturity. Photosynthesis and specific leaf N increased with increasing N rates, regardless of inoculation forms. Azospirillum-inoculated seeds, along with 114 kg ha−1 of N provided the maximum yield (6.0 Mg ha−1). Without N, seed inoculation increased yield by 33% and photosynthesis by 31% compared to the control. Averaged across N rates, seed inoculation increased photosynthetic N use efficiency by 17%–24% compared to the control. Azospirillum-inoculated seeds improved N use efficiency compared to the control under low N available. The results show that the Azospirillum-inoculated seeds could potentially decrease N rates from 180 to 114 kg ha−1 of N, while increasing yield by 9%. Besides, Azospirillum-inoculated seeds could enhance photosynthetic N use efficiency in a sustainable manner on low-fertility soils. Therefore, the benefits of the A. Brasiliense drive a sustainable agriculture by improving yield and reducing the need for N inputs.

Effect of sesame cake fertilizer with γ-PGA on soil nutrient, water and nitrogen use efficiency

γ-polyglutamic acid (γ-PGA), as an environmentally sustainable material, is extensive applied in agriculture for enhancing water and fertilizer utilization efficiency, augmenting crop yield, and ameliorating soil conditions. However, the effect of γ-PGA in conjunction with sesame cake fertilizer on the soil environment remains uncertain.The aim of this study is to investigate the effect of γ-PGA on soil nutrients, water use efficiency (WUE) and nitrogen use efficiency (NUE), and maize yield across various levels of sesame cake fertilizer. Additionally, the study seeks to identify the optimal ratio to establish a theoretical and practical foundation for sustainable agricultural development and the promotion of ecological agriculture. Through field experiments, nine treatments were established, comprising three levels of sesame cake fertilizer application rates (B1 = 900 kg/hm2 for low fertility, B2 = 1100 kg/hm2 for medium fertility, and B3 = 1300 kg/hm2 for high fertility) and three levels of γ-PGA application rates (R1 = 200 kg/hm2, R2 = 400 kg/hm2, and R3 = 600 kg/hm2). The results can be outlined as follows: (1) When γ-PGA application rate increased, total nitrogen (TN) exhibited a synergistic effect under B1 treatment, but an antagonistic effect under B2 and B3 treatments. At the 6-leaf stage (V6), 12-leaf stage (V12), and tasseling stage (VT), available phosphorus (AP) exhibited antagonistic effects. However, at the filling stage (R2) and maturity stage (R6), AP in B1 and B2 treatments at various depths underwent partial transformation into a synergistic effect. The levels of available potassium exhibited a notable antagonistic effect, leading to a decrease in harvest index (HI). B2 treatment demonstrated superior results compared to the B1 and B3 treatments, with the highest levels observed under B2R1 treatment; (2) TN content in the 0–40 cm soil layer increased during the filling period, and it was uniformly distributed in the 40–60 cm soil layer. When the soil AP was located in the 0–60 cm soil layer, there was an increase in AP content during the mature period. Following the tasseling period, different treatments exhibited varying patterns of increase in response to the presence of potassium within the 0–60 cm soil layer. Consequently, in cases where the sesame cake fertilizer content is low, the interaction between γ-PGA can compensate for the deficiency of fertilizer, thereby enhancing water and nitrogen utilization efficiency. The optimal fertilization strategy for enhancing soil nutrient distribution, WUE and NUE, and yield is proposed to be the application of 1100 kg/hm2 sesame cake fertilizer and 200 kg/hm2 γ-PGA.

An optimal combined slow-release nitrogen fertilizer and urea can enhance the decomposition rate of straw and the yield of maize by improving soil bacterial community and structure under full straw returning system.

Under a full straw returning system, the relationship between soil bacterial community diversity and straw decomposition, yield, and the combined application of slow-release nitrogen and urea remains unclear. To evaluate these effects and provide an effective strategy for sustainable agricultural production, a 2-year field positioning trial was conducted using maize as the research object. Six experimental treatments were set up: straw returning + no nitrogen fertilizer (S1N0), straw returning + slow-release nitrogen fertilizer:urea = 0:100% (S1N1), straw returning + slow-release nitrogen fertilizer:urea = 30%:70% (S1N2), straw returning + slow-release nitrogen fertilizer:urea = 60%:40% (S1N3), straw returning + slow-release nitrogen fertilizer:urea = 90%:10% (S1N4), and straw removal + slow-release nitrogen fertilizer:urea = 30%:70% (S0N2). Significant differences (p < 0.05) were observed between treatments for Proteobacteria, Acidobacteriota, Myxococcota, and Actinobacteriota at the jointing stage; Proteobacteria, Acidobacteriota, Myxococcota, Bacteroidota, and Gemmatimonadota at the tasseling stage; and Bacteroidota, Firmicutes, Myxococcota, Methylomirabilota, and Proteobacteria at the maturity stage. The alpha diversity analysis of the soil bacterial community showed that the number of operational taxonomic units (OTUs) and the Chao1 index were higher in S1N2, S1N3, and S1N4 compared with S0N2 at each growth stage. Additionally, the alpha diversity measures were higher in S1N3 and S1N4 compared with S1N2. The beta diversity analysis of the soil bacterial community showed that the bacterial communities in S1N3 and S1N4 were more similar or closely clustered together, while S0N2 was further from all treatments across the three growth stages. The cumulative straw decomposition rate was tested for each treatment, and data showed that S1N3 (90.58%) had the highest decomposition rate. At the phylum level, straw decomposition was positively correlated with Proteobacteria, Actinobacteriota, Myxococcota, and Bacteroidota but significantly negatively correlated with Acidobacteriota. PICRUSt2 function prediction results show that the relative abundance of bacteria in soil samples from each treatment differed significantly. The maize yield of S1N3 was 15597.85 ± 1477.17 kg/hm2, which was 12.80 and 4.18% higher than that of S1N1 and S0N2, respectively. In conclusion, a combination of slow-release nitrogen fertilizer and urea can enhance the straw decomposition rate and maize yield by improving the soil bacterial community and structure within a full straw returning system.

Multitemporal Field-Based Maize Plant Height Information Extraction and Verification Using Solid-State LiDAR

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize’s original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method’s ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland.

Maize tassel number and tasseling stage monitoring based on near-ground and UAV RGB images by improved YoloV8

Maize tassel number and tasseling stage monitoring based on near-ground and UAV RGB images by improved YoloV8

Heat‐stress‐induced fertility loss in summer maize (Zea mays L.): Quantitative analysis of contributions from developmental and physiological damage to pollen

AbstractIn recent years, the frequency and intensity of exposure to heat stress have increased gradually, seriously hampering the production of maize. This paper presents a study designed to analyze how the development, physiology and dispersal of pollen from the heat‐resistant maize variety Zhengdan958 and the heat‐sensitive maize variety Xianyu335 are affected by exposure to heat stress during the tasselling stage. Our results showed That exposure to heat stress significantly increased the antioxidant enzyme activity in pollen. Upon visual inspection of the pollen, we found that the lower water content had given rise to wrinkles in the pollen surface, sunken germination pores, and morphological deformations. In addition, the anther dehiscence process was hindered, resulting in a reduced amount of pollen being dispersed. We also found elevated levels of abscisic acid (ABA), jasmonic acid (JA) and its derivatives, indole acetic acid (IAA) and gibberellin‐3 (GA3) in heat‐pollens, as well as elevated ratios of IAA to ABA and ABA to GA3. Ultimately impaired pollen fertility. Summarizing, our experiment revealed that reduced pollen quantity and quality are significant contributors to fertility losses in summer maize exposed to heat stress at anthesis, and we hope that our analysis of the physiological mechanisms involved can contribute to the development of crop management measures aimed at countering the increasingly detrimental effects of heat stress on the production of summer maize.

Effects of slow-release nitrogen and urea combined application on soil physicochemical properties and fungal community under total straw returning condition

Under the system of full straw returning, the relationship between soil fungal community diversity and soil physiochemical properties, and the combined application of slow-release nitrogen and urea is unclear. To evaluate its effect and provide an effective strategy for sustainable agricultural production, a 2-year field positioning trial was conducted using maize as the research object. The experiment was designed with two factors: straw treatment(S) and nitrogen fertilizer treatment(N),Six experimental treatments were set up,S1N0,S1N1,S1N2,S1N3,S1N4,S0N2,respectively.Analysis of 54 soil samples revealed 15 fungal phyla and 49 fungal classes. The composition of fungal communities in each treatment was basically the same, but there were significant differences in species abundance. Under total straw returning conditions, the combined application of slow-release nitrogen fertilizer and normal nitrogen fertilizer significantly increased the relative abundance of Ascomycota. During the jointing stage, tasseling stage and maturity stage, S1N4, S1N3 and S1N2 increased by 25.76%, 22.97%, 20.74%; 25.11%, 30.02%, 23.64% and 22.47%, 28.14%, 22.71% respectively compared with S0N2.The relative abundance of Basidiomycota was significantly reduced. Alpha diversity analysis showed that the straw returning mode significantly increased the Shannon index and decreased the Simpson index, which was obvious in the jointing stage and tasseling stage. The principal coordinate analysis analysis results showed that the fungal communities formed different clusters in the horizontal and vertical directions at the three growth stages of corn jointing, tasseling and maturity. At the jointing stage and tasseling stage, the communities of the straw return treatment and the straw removal treatment were separated, and the community distribution of each treatment was not significantly different in the mature stage. Total straw returning combined with slow-release fertilizer significantly (P＜0.05) increased the soil organic carbon, nitrate nitrogen and ammonia nitrogen content in each growth period, and increased the soil total nitrogen and hydrolyzable nitrogen content (P＞0.05).After the straw was returned to the field, the combined application of slow-release nitrogen fertilizer and common urea had a significant impact on soil urease, catalase, and sucrase activities. Among them, the three enzyme activities were the highest in the S1N3 treatment at the jointing stage and maturity stage, and the S1N4 treatment at the tasseling stage had the highest enzyme activity. Fungal community composition is closely related to environmental factors. Soil organic carbon, urease and catalase are positively correlated with Ascomycota and negatively correlated with Basidiomycota.

Shading and waterlogging interactions exacerbate summer maize yield losses by reducing assimilate accumulation and remobilization processes

Persistent overcast rain was an essential limiting factor for summer maize production, of which immediate impact was the dual pressure of waterlogging and shading. However, the mechanism of independent and combined effects of waterlogging and shading induced maize yield losses are rarely studied, especially at different growth stages. Denghai 605 (DH605) was selected to be subjected shading, waterlogging, and their combined stress at the 3rd leaf stage (V3), the 6th leaf stage (V6), and tasseling stage (VT). Results showed that shading, waterlogging and their combination significantly limited the expansion of leaf area, and decreased leaf net photosynthetic rate (Pn) and net assimilation rate (NAR), thus reducing the crop growth rate (CGR) and biomass accumulation. At the same time, compared to control, the process of lignin synthesis was inhibited under stressed treatment, resulting in reduced stem mechanical strength and a poor development of the vascular system, of which change significantly reduced efficiency of assimilate remobilization to the ear and ultimately grain yield. The most significant effects of waterlogging and combined stresses on yield were occurred at V3 stage, followed by the V6 and VT stages. The most significant effects of shading were occurred at VT stage, followed by the V6 and V3 stages. Moreover, the compound stress exacerbated the damage brought about by a single stress. It is predicted that climate change will increase the frequency of abiotic stress assemblages, and the results of these findings provide some direction for further research on maize breeding in summer maize under continuous rainy conditions in the future.

The complex stress of waterlogging and high temperature accelerated maize leaf senescence and decreased photosynthetic performance at different growth stages

AbstractThe greenhouse effect caused by global warming was becoming more and more obvious, resulting in increased frequency of high temperature and high humidity, which significantly affected maize productivity. However, it was poorly understood how the interactions of high temperature and high humidity affected leaf senescence, photosynthetic performance and yield of summer maize. Three stress treatments including (a) high temperature stress (T), (b) waterlogging stress (W) and (c) complex stress (T‐W) were set at the third leaf stage (V3), the sixth leaf stage (V6) and the tasselling stage (VT) in 2019–2020 to explore the influence mechanism of complex stress. Each stress treatment period lasted 6 days. Non‐stressed plants served as control. Yield, antioxidant enzyme activity, photosynthetic characteristics, and dry matter accumulation were determined. Our study found that the activity of antioxidant enzymes was significantly decreased, while malonyldialdehyde (MDA) accumulation was increased under each stress treatment. As a result, the photosynthetic characteristics were impaired, manifested in a significant decrease in net photosynthetic rate (Pn), enzyme activities of phosphoenolpyruvate carboxylase (PEPCase) and ribulose diphosphate carboxylase (RUBPCase). The decrease in photosynthetic intensity affected by each stress treatment led to a significant decrease in total dry matter accumulation and grain yield. The most significant effects of waterlogging and combined stresses on yield occurred at the V3 stage, followed by the V6 and VT stages. However, the most significant effects of high temperature occurred at the VT stage, followed by the V6 and V3 stages. Moreover, the compound stress exacerbated damage to leaf senescence and photosynthetic properties of summer maize compared to the single stress of high temperature or waterlogging.

Automatic Modeling Prediction Method of Nitrogen Content in Maize Leaves Based on Machine Vision and CNN

Existing maize production is grappling with the hurdles of not applying nitrogen fertilizer accurately due to subpar detection accuracy and responsiveness. This situation presents a significant challenge, as it has the potential to impact the optimal yield of maize and ultimately, the profit margins associated with its cultivation. In this study, an automatic modeling prediction method for nitrogen content in maize leaves was proposed based on machine vision and convolutional neural network. We developed a program designed to streamline the image preprocessing workflow. This program can process multiple images in batches, automatically carrying out the necessary preprocessing steps. Additionally, it integrates an automated training and modeling system that correlates the images with nitrogen content values. The primary objective of this program is to enhance the accuracy of the models by leveraging a larger dataset of image samples. Secondly, the fully connected layer of the convolutional neural network was reconstructed to transform the optimization goal from classification based on 0–1 tags into regression prediction, so that the model can output numerical values of nitrogen content. Furthermore, the prediction model of nitrogen content in maize leaves was gained by training many samples, and samples were collected in three key additional fertilizing stages throughout the growth period of maize (i.e., jointing stage, bell mouth stage, and tasseling stage). In addition, the proposed method was compared with the spectral detection method under full-wave band and characteristic wavelengths. It was verified that our machine vision and CNN (Convolutional Neural Network)-based method offers a high prediction accuracy rate that is not only consistently better—by approximately 5% to 45%—than spectral detection approaches but also features the benefits of easy operation and low cost. This technology can significantly contribute to the implementation of more precise fertilization practices in maize production, leading to potential yield optimization and increased profitability.

Efeitos na produtividade e no valor nutricional da silagem de milho tratada com fungicida foliar e inoculante microbiano na ensilagem

ABSTRACT The aim of this study was to evaluate the yield, the morphometric characteristics, and the nutritional value of corn with use of foliar fungicide associated with inoculant on ensiling on the chemical-fermentative characteristics of silage. The applications of fungicide pyraclostrobin + fluxpyroxade were carried out in vegetative-eight and tasseling stage, and the evaluations of plants occurred simultaneously at harvest (dough grain stage), during which application was made with inoculant consisting of strains of Lactobacillus buchneri and L. casei for silage production. The use of fungicide decreased the percentage of leaf area affected by Diplodia macrospora Earle, Cercospora zeae-maydis and Phaeosphaeria maydis. At harvest, corn showed fewer dry leaves and higher yields of fresh (66,368vs. 62,015kg·ha-1) and dry biomass (20,964 vs. 19,485kg·ha-1) with fungicide. The fungicide also reduced the LDA content from 5.99% to 5.16%, which generated greater ISDMD for whole plant (43.14 and 62.57%, for 24 and 48 hours, respectively). The association of fungicide with inoculant promoted higher concentration of acetic acid than when each was used alone, and the dry matter losses of silage with inoculant were higher than control silage (8.88vs. 12.78%, respectively). Fungicide and inoculant used in combination provided silages with lower fibrous content.

Effects of seed grading on population regularity degree and yield of summer maize

Grading seeds based on grain size is an effective measure to improve population regularity degree and increase the yield of summer maize. Taking Denghai 605 as the experimental material, we set up a field experiment with treatments based on grain size: large seeds (L), medium-round seeds (MR), medium-flat seeds (MF), medium-round and medium-flat mixed seeds (MRF), and small seeds (S), with no-grading seeds as control (CK). We investigated seedling emergence rate, population regularity degree (including height, ear height and stem diameter), seedling sturdiness index, photosynthetic characteristics, dry matter accumulation and distribution characteristics, and yield. The results showed that the emergence rate followed an order of L>MR>MRF>MF>CK>S, with that of L treatment differed little from MR, MF and MRF treatments, but being significantly higher than S and CK treatments. Plant height and stem diameter population regularity degree of MRF treatment before seven-leaf stage was not different from those of L, MR, MF and S treatments, but significantly higher than those of CK. At the tasseling stage, all treatments had higher population regularity degree of plant height than other stages. Ear height population regularity degree of L, MR, MF, MRF, and S increased by 11.1%, 10.3%, 9.5%, 7.1%, and 6.4% compared with CK, respectively. The seedling sturdiness index of MRF treatment increased by 36.7% compared with S treatment, but was not significantly different from L treatment. The leaf area index of the L and MRF treatments was significantly higher than that of CK, and both had higher population photosynthetic properties. The population dry matter accumulation showed a pattern as L>MR>MRF>MF>CK>S. There was no significant difference among L, MR, and MRF treatments, but that in L being obviously higher than MF, CK, and S treatments. After seed grading, the number of harvested ears of the L and MRF treatments increased significantly, and the yield were shown as L>MR>MRF>MF>CK>S. There was no difference between the yield of MRF, MR and MF treatments. In conclusion, the performance of L treatment was improved but the number was small. Considering the grading cost and yield, the MRF treatment can save the seed amounts of sowing, realize mechanized sowing and precision sowing.

UAV-based plant height estimation of maize cultivated using different varieties and sowing spacing

This study aimed at estimating plant height of maize using low-cost unmanned aerial vehicle (UAV) based imagery. The experiment was laid out in a factorial arrangement in randomized complete block design. The factors were maize varieties (honampa, ahooden, ahoofe and abontem) and intra-row sowing spacing (20 cm, 30 cm and 40 cm). A constant inter-row spacing of 80 cm was used for all sub-plots. Data (both UAV-based imagery and manual data) were taken 21 days after sowing (DAS) -seedling/establishment stage-, 42 DAS (vegetative stage), 63 DAS (tasseling stage), and 84 DAS (physiological maturity stage). The results showed a high correlation between the UAV-based plant height and manual measurement with R2 (adjust) &gt; 0.92 in all cases. For the intra-row sowing spacing, the 20 cm plant spacing was found to have had the best overall R2 (adjust) of 0.949, showing the strongest regression of UAV with the manual data among the treatments. Also, it was found that the best stage to estimate maize plant height is dependent on the variety being used since the honampa gave the best correlation coefficient (R = 0.85) at the tasseling stage while the ahooden, ahoofe and abontem gave optimum correlation coefficients (R) of 0.70, 0.90 and 0.69 respectively at the vegetative stage. Overall, the study shows potency of using UAV-based imagery for estimating plant height of maize. The findings of the study could be useful to agronomists and smallholder farmers in Ghana and other developing countries regarding the usefulness of the technology for in-field data collection and advisory services.