Growth Stages Of Maize Research Articles

UAV multispectral remote sensing for the estimation of SPAD values at various growth stages of maize under different irrigation levels

Chlorophyll is crucial for photosynthesis in plants and the readings by a SPAD meter (Soil and Plant Analyzer Development) can be used to represent leaf chlorophyll content for monitoring crop growth status and predicting grain yield. Remote sensing technology has shown potential in non-destructive monitoring of SPAD values over large areas, but current SPAD inversion models are limited in their ability to incorporate multiple principal components besides spectral parameters, adapt to other variables such as water stress, and predict SPAD only throughout the entire growth period. This two-year study used crop parameters (plant height and leaf area index) and vegetation indices (VI) derived from unmanned aerial vehicle (UAV) multispectral images to develop SPAD prediction models for maize under different irrigation levels in the 2018 and 2019 growing seasons in Inner Mongolia, China. Two nonlinear machine learning models, random forest (RF) and support vector regression (SVR), and a multiple statistical regression method (partial least squares regression (PLSR)) were used to modeling SPAD. The results showed that the VIs with a high correlation with SPAD varied at each growth stage and the accuracy of SPAD estimation model can be improved significantly by dividing different growth stages (R2 increased by more than 104 %). PLSR performed better than RF and SVR for each growth period, especially at the reproductive R stage (R2 = 0.79, RMSE = 2.25). LAI and PH did not always improve prediction accuracy, but adding crop parameters did increase the correlation coefficient between predicted values and biomass by 8.3 %. This study provides valuable insights into the estimation of SPAD at different growth stages of maize under varying water stress levels using UAV data and crop parameters, offering guidance for farmland management and yield prediction.

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

Accurately determining the stage of crop development holds significant importance for field crop management. With the advancement of smart agriculture, an increasing number of Internet of Things (IoT) devices are being integrated into agricultural production, enabling more efficient acquisition of high-precision crop images. Currently, research on detecting crop growth stages based on IoT device images remains relatively scarce. Most existing studies rely on a single network model for detection, often encountering issues such as low accuracy and overfitting. Therefore, in this study, we collected maize images using IoT devices and constructed an integrated deep learning model by utilizing four convolutional neural networks (CNNs) to detect the growth period of maize in real time. Additionally, we implemented several improvements on these four CNNs and subsequently tested the performance of the ensemble model on the maize dataset. Regarding the ensemble strategy for the ensemble model, we proposed a dynamic weighted voting method, building upon the original voting approach, which can mitigate model training fluctuations and expedite model convergence. Ultimately, we manually simulated various lighting conditions to assess their impact on the ensemble model. Experimental results demonstrate that the ensemble deep model proposed in this paper represents a robust method for detecting maize growth stages, achieving an accuracy rate of 0.976 on the maize dataset, effectively facilitating high-precision detection of maize growth stages in complex backgrounds.

Estimating Summer Maize Biomass by Integrating UAV Multispectral Imagery with Crop Physiological Parameters.

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R2) ranging from 0.516 to 0.649. The R2 of the RF (CIS) model is 0.446-0.537. The R2 of the PLSR (CIS) model is 0.323-0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R2 increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R2 = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles.

Simulation and Evaluation of Spring Maize Growth Under Drip Irrigation with Fully Biodegradable Film Mulching Based on the DSSAT Model.

Fully biodegradable mulch film enhances temperature and moisture retention during the early stages of maize growth while naturally degrading in the later stages, providing an environmentally friendly alternative to conventional plastic mulch films. However, there is no consensus on its impact on maize growth and yield. The present study utilized field test data from spring maize covered with fully biodegradable mulch film in the Xiliaohe Plain, aiming to improve the Decision Support System for Agrotechnology Transfer (DSSAT) model while focusing on soil temperature, irrigation, rainfall, and evapotranspiration. The parameters of the DSSAT model were calibrated and validated using field test data from 2016 to 2018. The improved DSSAT model accurately simulated the maize growth process under various induction periods of fully biodegradable mulch film. The simulation accuracy of this model was as follows: MRE < 10%, nRMSE < 12%, and R2 ≥ 0.80. Moreover, the yield of spring corn covered with fully biodegradable mulch film was predicted using meteorological data from 2019 to 2023. This study suggests that regions such as the Xiliaohe Plain, which share climatic conditions, should opt for fully biodegradable mulch film with an induction period of approximately 80 days to ensure high yields across different hydrological years.

Effects of plastic film mulching on soil microbial carbon metabolic activity and functional diversity at different maize growth stages in cool, semi-arid regions.

Plastic film mulching has been widely used to enhance soil hydrothermal conditions and increase crop yields in cool, semi-arid areas. However, its impact on soil microbial carbon metabolic activity and functional diversity during plant growth remains unclear despite their important roles in nutrient cycling and soil quality evaluation. This study used the Biolog EcoPlate technique to investigate the dynamics and driving factors of soil microbial carbon metabolic activity and functional diversity at different maize growth stages following plastic film mulching. The results revealed that film mulching significantly increased microbial carbon metabolic activities [represented by average well color development (AWCD)] by 300% at the seedling stage and by 26.8% at maturity but decreased it by 47.4% at the flowering stage compared to the control (without mulching). A similar trend was observed for the microbial functional diversity index. Redundancy analysis identified soil moisture (SM), soil temperature (ST), dissolved organic carbon (DOC), microbial biomass carbon (MBC), and bacteria amounts as the primary factors influencing changes in soil microbial carbon source utilization. The mulch treatment significantly increased SM at all growth stages, while its warming effect disappeared at the flowering stage. Soil DOC, MBC, and bacterial populations were notably higher under mulching at the seedling and maturity stages but lower at the flowering stage. Pearson correlation analysis showed that changes in SM, ST, DOC, MBC, and bacterial populations positively correlated with the utilization of all carbon source classes, AWCD, and functional diversity indexes after film mulching. Furthermore, maize grain yield and water use efficiency increased by 142 and 129%, respectively, following film mulching. In conclusion, plastic film mulching enhanced soil microbial carbon metabolic activity and functional diversity at the seedling and maturity stages, improving crop yields in cool, semi-arid areas. Furthermore, the decrease in soil carbon metabolic capacity at flowering stage highlights that supplementing soil carbon sources should be considered after continuous film mulching to sustain or enhance farmland productivity and soil quality.

Assessment of waterlogging hazard during maize growth stage in the Songliao plain based on daily scale SPEI and SMAI

Waterlogging is one of the major disasters affecting crop yield and food security. The Songliao Plain, located in the mid-latitude region and known as the "Golden Maize Belt," is severely impacted by waterlogging, which significantly affects maize yield. Therefore, it is essential to conduct a detailed assessment of the waterlogging hazard for maize in the Songliao Plain and to apply the results to agricultural meteorological disaster prevention and mitigation measures. In this study, a comprehensive waterlogging hazard assessment index was constructed by combining environmental factors conducive to disaster and disaster-causing factors. Environmental factors included terrain slope, distance from rivers, and soil clay content, while disaster-causing factors included daily SPEI and SMAI during the maize growing season in the Songliao Plain from 1982 to 2020. The results indicate that: (1) The spatial distribution of waterlogging hazard in the Songliao Plain ranges from extremely high to extremely low, showing a gradual decrease from west to east. The western and southern parts of the Songliao Plain, such as Baicheng, Songyuan, Changchun, and Fuxin, are more prone to waterlogging disasters. (2) During different maize growth stages, the spatial distribution of high and extremely high levels of waterlogging hazard exhibited significant heterogeneity. There were notable differences in the duration of waterlogging around the year 2000, with a reduction in the duration of extremely high and high levels of waterlogging after 2000. (3) A Pearson correlation analysis was conducted between the comprehensive waterlogging hazard index and SIF (Solar-Induced Fluorescence) data during different maize growth stages. The results showed a strong correlation between the comprehensive waterlogging hazard index and SIF data, with the highest correlation coefficient reaching −0.9 and a p-value less than 0.05. The comprehensive maize waterlogging hazard index can be used for precise and timely assessment of waterlogging hazard during different growth stages of maize, and it has a positive impact on improving the ability to prevent and mitigate waterlogging risks.

Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features.

Machine learning and fluorosensing for estimation of maize nitrogen status at early growth-stages

Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluorescence channels and indices as predictive variables for estimating plant nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) often fails to deliver a good range of prediction accuracies. Hence, machine learning regression techniques are utilized in this study to estimate N status indicators i.e., %N, above ground biomass, and N uptake at V6 and V9 growth stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex®3 (FORCE-A) portable active fluorescence system was used to capture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlorophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as predictors. The independent site data were first utilized in a Support Vector Regression (SVR) model to assess the training and test accuracies in estimation of N status indicators considering a comparative analysis between V6 and V9 growth stages. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performance in a cross-site experimental setting. Subsequently, cross-site comparisons of nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regression, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random Forest Regression, and (5) Artificial Neural Network. The comparative investigation demonstrated promising accuracy in estimating plant nitrogen content, above-ground biomass, and nitrogen uptake at the V6 stages of maize, with correlation coefficients in the moderate range (r = 0.72 ± 0.03) and Root Mean Square Error. Superior prediction accuracies were obtained at V9 growth stages than at V6. Among the various machine learning models assessed, Support Vector Regression consistently outperformed the others across the three site-year datasets, delivering error estimates well within acceptable ranges and demonstrating the lowest RMSE values for maize nitrogen indicators.

Impact of Maize Nutrient Composition on the Developmental Defects of Spodoptera frugiperda

Spodoptera frugiperda (J.E. Smith) is a crucial agricultural pest owing to its global impact on &gt;300 crops. Among these, the corn strain of S. frugiperda causes significant damage to maize (Zea mays L.). However, limited research exists on the influence of maize nutrients on the metamorphosis of S. frugiperda and the underlying mechanisms. In this study, the effects of different growth stages of maize leaves, namely, tender leaves (tender) and mature leaves (mature), on various aspects of larval development, including body weight, body length, developmental age, pupation rate, and eclosion rate, were investigated. Additionally, we measured the levels of 20-hydroxyecdysone (20E) and three types of juvenile hormone (JH; i.e., JH I–III) in S. frugiperda larvae fed on tender or mature. The results revealed that larvae fed on Tender exhibited significantly prolonged instar duration, reduced body weight and length, and decreased pupation and eclosion rates, with the emergence of abnormal adults. Analysis of nutritional components in maize leaves revealed significantly higher levels of amino acids, soluble sugars, and sterols in mature than in tender. Hormone analysis in S. frugiperda larvae revealed higher 20E titers in individuals feeding on mature during prepupal and pupal stages. We demonstrated the crucial role of sterols in regulating the level of 20E and pupation rate of S. frugiperda. Based on these findings, we propose that isoleucine, arginine, glutamic acid, sucrose, campesterol, and β-sitosterol serve as key nutrients influencing the development of S. frugiperda. Moreover, β-sitosterol is a significant factor influencing the interaction between maize leaves and S. frugiperda. Our research results provide a reference for the control strategy of S. frugiperda based on breeding insect-resistant varieties by altering host nutrition.

A CNN- and Self-Attention-Based Maize Growth Stage Recognition Method and Platform from UAV Orthophoto Images

The accurate recognition of maize growth stages is crucial for effective farmland management strategies. In order to overcome the difficulty of quickly obtaining precise information about maize growth stage in complex farmland scenarios, this study proposes a Maize Hybrid Vision Transformer (MaizeHT) that combines a convolutional algorithmic structure with self-attention for maize growth stage recognition. The MaizeHT model utilizes a ResNet34 convolutional neural network to extract image features to self-attention, which are then transformed into sequence vectors (tokens) using Patch Embedding. It simultaneously inserts category information and location information as a token. A Transformer architecture with multi-head self-attention is employed to extract token features and predict maize growth stage categories using a linear layer. In addition, the MaizeHT model is standardized and encapsulated, and a prototype platform for intelligent maize growth stage recognition is developed for deployment on a website. Finally, the performance validation test of MaizeHT was carried out. To be specific, MaizeHT has an accuracy of 97.71% when the input image resolution is 224 × 224 and 98.71% when the input image resolution is 512 × 512 on the self-built dataset, the number of parameters is 15.446 M, and the floating-point operations are 4.148 G. The proposed maize growth stage recognition method could provide computational support for maize farm intelligence.

Maize Pest and Natural Enemies in the North-West of Türkiye

Maize Pest and Natural Enemies in the North-West of Türkiye ABSTRACT Maize is one of the most significant cereal crops in the world, and insect pests cause the highest economic loss. The objective of this study was to assess the level of insect pests and natural enemies during 2020 and 2022 in the maize fields of Düzce and Sakarya, Türkiye. We performed weekly surveys from the vegetative growth stage of maize to harvest from April through November. A hundred plants were selected with regular and irregular samplings from each field. In the maize fields, a total of 13 pest species from six families in four orders, along with 19 natural enemies from eight families in five orders, were identified. Ostrinia nubilalis Hbn. (Lepidoptera: Crambidae) was found as the primary pest, followed by Helicoverpa armigera Hbn. and Mythimna unipuncta Haw. (Lepidoptera: Noctuidae). Meteorus pendulus Müller (Hymenoptera: Braconidae) was a new record for the East Marmara and Western Black Sea regions, as well as a new host record in Türkiye. Larval parasitoids of M. unipuncta, Nemoraea pellucida Meigen and Pales pavida Meigen (Diptera: Tachinidae) represent new host records for Türkiye. Among the predators, Orius minutus L. (Hemiptera: Anthocoridae) and coccinellids showed an especially notable common in the maize fields of both provinces. Ketwords: Maize, pests, natural enemies, pest status

Magnesium-doped biochars increase soil phosphorus availability by regulating phosphorus retention, microbial solubilization and mineralization

Despite fertilization efforts, phosphorus (P) availability in soils remains a major constraint to global plant productivity. Soil incorporation of biochar could promote soil P availability but its effects remain uncertain. To attain further improvements in soil P availability with biochar, we developed, characterized, and evaluated magnesium-oxide (MgO) and sepiolite (Mg4Si6O15(OH)2·6H2O)-functionalized biochars with optimized P retention/release capacity. Field-based application of these biochars for improving P availability and their mechanisms during three growth stages of maize was investigated. We further leveraged next-generation sequencing to unravel their impacts on the plant growth-stage shifts in soil functional genes regulating P availability. Results showed insignificant variation in P availability between single super phosphate fertilization (F) and its combination with raw biochar (BF). However, the occurrence of Mg-bound minerals on the optimized biochars’ surface adjusted its surface charges and properties and improved the retention and slow release of inorganic P. Compared to BF, available P (AP) was 26.5% and 19.1% higher during the 12-leaf stage and blister stage, respectively, under MgO-optimized biochar + F treatment (MgOBF), and 15.5% higher under sepiolite-biochar + F (SBF) during maize physiological maturity. Cumulatively, AP was 15.6% and 13.2% higher in MgOBF and SBF relative to BF. Hence, plant biomass, grain yield, and P uptake were highest in MgOBF and SBF, respectively at harvest. Optimized-biochar amendment stimulated microbial 16SrRNA gene diversity and suppressed the expression of P starvation response and P uptake and transport-related genes while stimulating P solubilization and mineralization genes. Thus, the optimized biochars promoted P availability via the combined processes of slow-release of retained phosphates, while inducing the microbial solubilization and mineralization of inorganic and organic P, respectively. Our study advances strategies for reducing cropland P limitation and reveals the potential of optimized biochars for improving P availability on the field scale.Graphical

The impact of mineral fertilisers on the physicochemical properties of soil in maize cultivation

The use of mineral fertilisers is a critical factor in modern agriculture, significantly influencing the physicochemical properties of soil, which in turn affects crop yield and quality. Understanding these impacts is essential for sustainable agricultural practices. This study aimed to determine the effects of different types and quantities of mineral fertilisers on the physicochemical properties of soil in the context of maize cultivation. The research involved experimental field trials with varying applications of mineral fertilisers. Soil samples were collected at different stages of maize growth and analysed for parameters such as pH, electrical conductivity, organic matter content, and nutrient availability (nitrogen, phosphorus, potassium). The study employed a range of methods to investigate the effects of mineral fertilisers on soil properties, including soil preparation, application of various types and doses of fertilisers, maize planting, plant growth monitoring, and analysis of soil physicochemical characteristics. The application of mineral fertilisers led to significant changes in soil pH, with some fertilisers causing acidification and others increasing alkalinity. Fertilised plots showed increased electrical conductivity, indicating a rise in soluble salt content. Variations in organic matter content were observed, dependent on the type and dosage of fertilisers used. It was determined that fertilised plots exhibited elevated levels of nitrogen, phosphorus, and potassium, directly correlating with the type and quantity of fertiliser applied. The highest maize yield was achieved with balanced applications of nitrogen-phosphorus-potassium (NPK) fertilisers, underscoring the importance of balanced nutrient management. These findings provide valuable insights for optimising fertiliser use, which may contribute to improved soil health, increased maize yield, and sustainable agricultural practices

Maize plant growth period identification based on MobileNet and design of growth control system

To address the current inefficiencies and subjective nature of manual observation in maize cultivation, with the aim of achieving high efficiency and productivity, this study focused on the DeMaya D3 maize variety. It proposes a maize growth stage recognition method based on the MobileNet model, which is a lightweight convolutional neural network architecture. The method was tested and achieved recognition accuracies of 0.98, 0.96, 0.92, 0.85, and 0.97 for different growth stages, respectively. Additionally, a maize growth prediction model was developed. Based on data collected from experimental plots regarding maize plant height and stem diameter, the Prophet model and an optimized version of the Prophet model were used to forecast maize growth trends. The Prophet model is an open-source tool for time series forecasting. Comparative analysis was conducted between the predictions of the original Prophet model and the optimized version. The relative errors of the Prophet model predictions were 0.85%, 2.11%, and 0.79%, while those of the optimized Prophet model were 0.76%, 0.47%, and 0.71%. Compared to the Prophet model, the optimized model reduced errors by 0.09%, 1.64%, and 0.08%, respectively. The maize plant growth control system was designed to obtain the information through the collection layer. The decision-making layer judged the soil nutrient absorption and growth status. Finally, the management layer controlled water and fertilizer.

Impact of glyphosate on the rhizosphere microbial communities of a double-transgenic maize line D105

Plant roots shape the rhizosphere microbiome, recruiting microbes with beneficial functions. While genetically engineered crops offer yield advantages, their impacts on rhizosphere microbial communities remain understudied. This study evaluated the effects of transgenic maize, alongside a non-transgenic counterpart, on rhizosphere bacterial and fungal community composition using 16S rRNA and ITS amplicon sequencing. Additionally, glyphosate was used to evaluate its impact on microbial assembly and the magnitude of its effect at various maize growth stages. The results showed that transgenic maize D105 line significantly increased bacterial alpha diversity but not fungal diversity. Beta diversity analysis showed clear separation between bacterial and fungal communities at higher glyphosate treatment. Specific bacterial taxa such as Pseudomonas and Sphingomonas were enriched, while fungal taxa such as Ascomycota, Lasiosphaeriaceae, Verticillium were differentially abundant in glyphosate treatments. LEfSe analysis identified distinct enrichment patterns of bacterial (Proteobacteria and Actinobacteria) and fungal taxa (Verticillium and Guehomyces) associated with the transgenic line and glyphosate levels. KEGG functional analysis suggested potential impacts on bacterial metabolic pathways and shifts in fungal trophic modes (saprotrophs, pathogens) within the rhizosphere microbiome. This research provides insights into the classification, functional relationships, and underlying mechanisms shaping microbial communities carrying insect resistance and glyphosate resistance traits.

Maize and legume intercropping enhanced crop growth and soil carbon and nutrient cycling through regulating soil enzyme activities

Maize intercropped with leguminous green manure (LGM) has been proven as a sustainable plantation practice for enhancing crop yield and soil fertility. However, a comprehensive understanding of how intercropped legumes coordinate the above- and below-ground performance during maize growth under low to high N application remains elusive. This study aimed to investigate the effects of biological C and N input on soil fertility and maize growth by regulating soil enzyme activities in maize/LGM intercropping systems. A three-year field trial was conducted in northwestern China, where maize was intercropped with two LGMs, namely common vetch and pea, under no N (N0, 0 kg ha−1) and conventional N (N330, 330 kg ha−1) applications. The grain yield of intercropped maize ranged from 10.54 to 11.08 t ha−1, representing a significant increase of 6.6–12.1 % compared to monoculture maize in the N0 treatment. Nitrogen application substantially increased maize grain yield by 39.2–52.0 % across cropping systems relative to the N0 treatments, while minor differences were observed in maize yield between cropping systems in the N330 treatments. Compared with monoculture maize, intercropped with LGMs increased C input by 16.7–79.2 % at maize V9 stage and 81.1–140.3 % at the R6 stage. The biological N fixation of intercropped LGMs was 62.7–89.5 kg ha−1 and 8.0–12.8 kg ha−1 in the N0 and N330 treatments, respectively. Biological C and N input greatly facilitated soil enzyme activities and the associated nutrient cycling, consequently improving soil fertility. Soil organic matter and total N in the intercropping systems significantly increased by 4.4–14.3 % relative to monoculture across maize growth stages, irrespective of N levels. Furthermore, increased soil fertility was closely associated with nutrient stoichiometry, which in turn facilitated maize nutrient uptake and shoot recovery growth in the intercropping systems. Overall, these findings highlight that increased biological C and N input to belowground enhances soil C and nutrient cycling by regulating the involved enzyme activities, which in turn improves maize nutrient uptake and growth, forming a coordinated loop of C and nutrient flow in the maize and legume intercropping systems. Maize intercropped with LGMs is a sustainable practice that improves soil fertility and promotes maize growth by augmenting biological C and N input.

Functionality of arbuscular mycorrhizal fungi varies across different growth stages of maize under drought conditions

Physio-biochemical regulations governing crop growth period are pivotal for drought adaptation. Yet, the extent to which functionality of arbuscular mycorrhizal fungi (AM fungi) varies across different stages of maize growth under drought conditions remains uncertain. Therefore, periodic functionality of two different AM fungi i.e., Rhizophagus irregularis SUN16 and Glomus monosporum WUM11 were assessed at jointing, silking, and pre-harvest stages of maize subjected to different soil moisture gradients i.e., well-watered (80% SMC (soil moisture contents)), moderate drought (60% SMC), and severe drought (40% SMC). The study found that AM fungi significantly (p < 0.05) affected various morpho-physiological and biochemical parameters at different growth stages of maize under drought. As the plants matured, AM fungi enhanced root colonization, glomalin contents, and microbial biomass, leading to increased nutrient uptake and antioxidant activity. This boosted AM fungal activity ultimately improved photosynthetic efficiency, evident in increased photosynthetic pigments and photosynthesis. Notably, R. irregularis and G. monosporum improved water use efficiency and mycorrhizal dependency at critical growth stages like silking and pre-harvest, indicating their potential for drought resilience to stabilize yield. The principal component analysis highlighted distinct plant responses to drought across growth stages and AM fungi, emphasizing the importance of early-stage sensitivity. These findings underscore the potential of incorporating AM fungi into agricultural management practices to enhance physiological and biochemical responses, ultimately improving drought tolerance and yield in dryland maize cultivation.

Effects of Different Proportions of Ammonium Sulfate Replacing Urea on Soil Nutrients and Rhizosphere Microbial Communities

In order to investigate the effects of ammonium sulfate, an industrial by-product, on soil nutrients and microbial community when applied in different proportions instead of using urea as nitrogen fertilizer, a pot corn experiment was conducted. A completely randomized block experimental design was used, with a total of five treatments:CK (no fertilization), U10S0 (100 % urea), U8S2 (80 % urea + 20 % ammonium sulfate), U6S4(60 % urea + 40 % ammonium sulfate), and U0S10 (100 % ammonium sulfate). The basic physical and chemical properties of soil and the dry weight of maize plants were determined by conventional methods, and microbial sequencing was performed using the Illumina NovaSeq platform. The experiment results showed that:① In each growth stage of maize, the pH of soil treated with fertilization (7.85-8.15) was decreased compared with that of CK (8.1-8.21), and the pH showed a decreasing trend with the increase in ammonium sulfate content. ② The soil available nitrogen content increased gradually with the increase in the ammonium sulfate ratio at each growth stage of maize. Compared with that in the CK and U10S0 treatments, the ratio in the U0S10 treatment increased 30.56 % to 63.68 % and 13.22 % to 38.43 %, respectively. The variation trend of organic carbon content was opposite to that of available nitrogen (U8S2 &gt; U6S4 &gt; U0S10), and the addition of ammonium sulfate was still higher than that of U10S0 at other growth stages except for the seedling stage. ③ The protease activity of all fertilization treatments was higher than that of the control, and the protease activity was gradually enhanced with the continuous growth of corn and the increase in the ammonium sulfate ratio. The protease activity of the U0S10 treatment was higher than that of the U10S0 treatment at each growth stage of corn, which increased by 10.54 %-100 %. Soil sucrase activity ranged from 0.04 to 0.24 mg·(g·24 h)-1, and those in the U0S10 treatments were significantly higher than those in the U10S0 and CK treatments at all growth stages, increasing by 20.32 % to 99.16 % and 24.31 % to 79.33 %, respectively. ④ The species abundance of bacteria and fungi in maize rhizosphere under all fertilization treatments were lower than those under the CK treatment, followed by those under the U10S0 treatment. The species diversity trend of the bacterial community in the three treatments with ammonium sulfate replacing urea were U8S2 &gt; U0S10 &gt; U6S4, and that of fungi were U6S4 &gt; U8S2 &gt; U0S10. ⑤ The maize dry weight of the U10S0 treatment and U0S10 treatment was the highest, which was 39.47 % and 36.16 % higher than that of the CK treatment, respectively, but the difference was not significant. The Pearson model showed that the species abundance and diversity of soil rhizosphere fungi and bacteria were affected by relevant environmental variables, among which pH value and soil available nitrogen content were the most important factors affecting microbial diversity. In conclusion, when corn planting in calcareous brown soil, replacing urea with a certain proportion of ammonium sulfate can improve soil nutrients more than urea alone, which affects the growth and rhizosphere microbial community of corn to a certain extent and has a greater yield.

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.

Boron Impact on Maize Growth and Yield: A Review

Maize (Zea mays L.) is a vital crop, contributing significantly—at least 30%—to global dietary energy intake and biofuels and ethanol production. This review article delves into the dynamic interplay between boron (B) and maize growth, yield, and agricultural sustainability. Boron, a crucial micronutrient, is pivotal in essential physiological processes such as root development, leaf expansion, and cob formation. These processes are fundamental for ensuring the vigour and productivity of maize crops. Conversely, boron deficiency manifests as thinner leaves with reduced chlorophyll content, compromising plant health, and hindering yield potential. Maintaining adequate boron levels, particularly during reproductive stages, is critical for mitigating the risk of abnormal ears and maximizing the quantity and quality of maize production. Emerging research underscores the significance of foliar boron application at various growth stages of maize, which stimulates growth, facilitates cell wall development, and increases leaf area. This translates to improved light interception and photosynthetic efficiency, ultimately contributing to increased plant vigour and biomass accumulation. Furthermore, exploring innovative approaches for sustainable boron management is crucial. This includes precision fertilization techniques and biofortification strategies to ensure optimal maize production while minimizing environmental impacts. By gaining a deeper understanding of the complex relationship between boron and maize, farmers can develop customized fertilization plans that utilize strategic foliar boron application. This approach unlocks maize's full yield potential and contributes to sustainable agricultural practices, supporting global food security.