Maize Crop Research Articles

The Effect of Border Crops and Physical Barriers on Incidence of Chilli Leaf Curl Complex and Associated Insect Populations

Chilli Leaf Curl (CLC) possesses a significant biotic threat leading to substantial yield losses in chilli cultivation. Various agents such as thrips, aphids, mites and the viruses transmitted by whiteflies have been identified as contributors to CLC. In order to mitigate this issue, three types of physical barriers were used: an insect-proof net (1 m high) surrounded by three rows of maize crops, an insect-proof net (1 m high) alone, and three rows of maize crops. A control was maintained without a physical barrier. The experiment was laid out as randomized complete block design with three replicates. Identification of pests and diseases, CLC disease incidence and yield attributes were assessed. Results revealed that the treatment with an insect-proof net combined with a maize border exhibited the lowest disease incidence (2±0.45, p=0.01) compared to chilli plants with a maize border only. The whitefly population varied across the treatments (p=0.03) and with the age of the crop (p<0.0001). Among the treatments, the control and insect-proof net exhibited a significantly lower population of whiteflies. Thrips did not show a significant response to the treatments (p=0.54) indicating that the presence of physical barriers had no notable effect on thrips counts. Additionally, there was no significant difference in the number of pods per plant (p=0.74) and yield per plant across the treatments (p=0.54). In conclusion, the use of insect-proof net with a maize border surrounding the chilli cultivation would help to develop integrated pest management strategies to reduce chilli leaf curl incidence as well as whitefly population.

Using Unmanned Aerial Systems Technology to Characterize the Dynamics of Small-Scale Maize Production Systems for Precision Agriculture

Precision agriculture (PA) utilizes spatial and temporal variability to improve the sustainability and efficiency of farming practices. This study used high-resolution imagery from UAS to evaluate maize yield variability across three fields in Ghana: Sombolouna, Tilli, and Yendi, exploiting the potential of UAS technology in PA. Initially, excess green index (EGI) classification was used to differentiate between bare soil, dead vegetation, and thriving vegetation, including maize and weeds. Thriving vegetation was further classified into maize and weeds, and their corresponding rasters were developed. Normal difference red edge (NDRE) was applied to assess maize health. The Jenks natural breaks algorithm classified maize rasters into low, medium, and high differential yield zones (DYZs). The percentage of bare spaces, maize, weed coverages, and total maize production was determined. Significant variations in field conditions showed Yendi had 34% of its field as bare, Tilli had the highest weed coverage at 22%, and Sombolouna had the highest maize crop coverage at 73.9%. Maize yields ranged from 860 kg ha−1 in the low DYZ to 4900 kg ha−1 in the high DYZ. Although yields in Sombolouna and Tilli were similar, both fields significantly outperformed Yendi. Scenario analysis suggested that enhancing management practices to elevate low DYZs to medium levels could increase production by 2.1%, while further improvements to raise low and medium DYZs to high levels could boost productivity by up to 20%.

Nitrogen cycling and associated grey water footprint in croplands under different irrigation practices

The pervasive application of nitrogen (N) fertilisers in agriculture poses a significant threat to freshwater ecosystems. The grey water footprint (GWF) is a measure of the volume of freshwater required to assimilate the relative water pollutants resulted from N leaching-runoff processes. The responses of N cycling and associated regional GWF to crop production under different irrigation methods have not been investigated. In this study, the N leaching-runoff rate, N₂O, and NH₃ emissions in croplands and the associated GWF per unit crop yield (UGWF) and annual total GWF (TGWF) of winter wheat-summer maize crop rotations across the Hebei, Shandong, and Henan Region (HSHR) of China were simulated and evaluated at the prefecture level over the 2004–2020 period. Four irrigation scenarios were modelled: furrow, sprinkler, surface drip, and subsurface drip irrigation. The results showed significant spatial and temporal heterogeneity in N leaching-runoff rates and emissions of N2O, and NH3. The N leaching-runoff rates for winter wheat and summer maize ranged from 3.9% to 10.9% and 15.0%–32.3%, respectively. With changing different irrigation methods, N leaching-runoff rates exhibited greater sensitivity than N₂O and NH₃ emissions; this sensitivity was more pronounced in winter wheat than in summer maize. Findings reveal that N pollution is influenced not only by irrigation methods but also by weather conditions, crop-specific rooting characteristics, sowing time, and soil texture. Winter wheat UGWF under the four irrigation methods mostly followed the order of furrow > sprinkler irrigation > surface drip irrigation > subsurface drip irrigation. Conversely, for summer maize, subsurface drip irrigation resulted in a higher UGWF compared to furrow irrigation, mainly due to increased water percolation thus higher N leaching-runoff rate in near-saturated soil with higher rainfall. This result reveals the potential conflict between water-saving irrigation and N pollution control. This analysis underscores the necessity of recognising the combined effects of agricultural management practices on water conservation and environmental safeguarding.

Assessment of Crop Loss Due to Elephant Crop Raids and Its Eco Friendly Mitigation

The crop raiding by elephants is one of the important concern causing loss of livelihood and affects the farmer’s economy. Hence a study was conducted to estimate the crop losses and to evolve an economically feasible mitigation measures. The studies were conducted in villages belonging to Kodihalli and Uyyamballi Hobli of Kanakapura Taluka, Ramanagara District, Karnataka which is surrounded by thick forest cover during 2022 to 2023. The estimation on crop loss revealed that the highest crop losses were recorded in banana (5.26 to 46.54%) plantations followed by maize (2.47 to 41.87%), ragi (2.31 to 38.95%), mango (1.28 to 32.54%), pigeon pea (1.31-21.41%) and cowpea (1.46 to 12.68%). The highest economic loss was recorded in banana and mango crops. The evaluation of mitigation measures in ragi and maize crop revealed the use of bioacoustics and predator deterrent lights provided better economical feasible mitigation measures with the cost benefit ratio of 1:2.9 and 1:3.5 respectively. The use of barbed wire fence and use of solar fence (Agri solar) was also found effective in mitigating the crop raids by elephants. However, these mitigation measures are short term and has to be used in vulnerable stages of the crop only.

Maize biomass yield variations due to diverse nitrogen applications using Aquacrop

AbstractIn the quest to increase crop yields and production intensity to meet the demands of a growing global population, the responsible use of nitrogen (N) applications is paramount. Excessive application of these fertilizers results in economic losses, environmental pollution and reduced N‐use efficiency. To address this challenge, precision agriculture techniques offer promising solutions. This study explored the relationships among applied N, maize crop parameters and the nitrogen nutrition index (NNI) to optimize N application usage and crop performance. The AquaCrop model was used to simulate water productivity and plant parameters accurately. The research establishes equations between the NNI and maize parameters, enabling the use of AquaCrop to simulate crop responses to varying N levels. The results demonstrate that AquaCrop effectively simulates more than 95% of the biomass changes. The findings suggest that applying N based on presented equations optimized the use of N applications, thereby mitigating economic losses and environmental impacts.

Detection and Monitoring of Damage Status and Infestation by Spodoptera frugiperda in Maize Crops in Luxor Region, Egypt

Detection and Monitoring of Damage Status and Infestation by Spodoptera frugiperda in Maize Crops in Luxor Region, Egypt

Assessment of Lepidopteran Pest Dynamics in Maize: Dominance of Spodoptera frugiperda and Implications for Targeted Management in Himachal Pradesh, India

Maize (Zea mays L.) plays a crucial role in the agricultural economy of India, particularly in Himachal Pradesh, where it serves as a vital crop for food, feed, and fodder. This study assessed the distribution, and relative abundance of lepidopteran pests infesting maize during the kharif, 2022-23 across two agro-climatic zones of Himachal Pradesh. Surveys identified five key lepidopteran pests: Spodoptera frugiperda, Chilo partellus, Mythimna separata, Agrotis ipsilon, and Euproctis spp. Among all, S. frugiperda is the only pest occurring among the major three crop stages of maize. The findings revealed that S. frugiperda was the most dominant pest, with relative abundance values of 19.57 per cent in Zone I and 18.94 per cent in Zone II during the seedling stage, increasing to 30.87 per cent and 30.40 per cent during the vegetative stage, respectively. In contrast, other pests like C. partellus, M. separata, A. ipsilon, and Euproctis spp. exhibited variable distribution patterns, with some showing limited occurrence. In conclusion, this study underscores the necessity for targeted pest management strategies to address dominant and subdominant lepidopteran pests in maize cultivation in Himachal Pradesh. The results offer valuable insights that can inform focused pest control efforts, with the potential to significantly enhance sustainable maize production, crop resilience and overall productivity in the region.

Model for Predicting Maize Crop Yield on Small Farms Using Clusterwise Linear Regression and GRASP

Planting a crop involves several key steps: resource assessment, crop selection, crop rotation, planting schedules, soil preparation, planting, care, and harvesting of crops. In this context, estimating the productivity of a crop based on available information, such as expected climatic conditions and agricultural practices, helps farmers reduce the uncertainty of their investment. In Colombia, maize is the fourth most important crop in the country. Significant efforts are required to improve productivity in traditional and technified production systems. In this sense, this research proposes and evaluates an approach called Clusterwise Linear Regression (CLR) to predict the crop maize yield in small farms, considering data on climate, soil, fertilization, and management practices, among others. To develop the CLR model, we conducted the following steps: data collection and preparation, clustering using k-means, cluster optimization with Greedy Random Adaptive Search Procedure (GRASP), and performance evaluation. The cluster optimization process allows the identification of clusters with similar characteristics and generates multiple linear regression models with mixed variables that explain the yield of the farms on each cluster. The Simulated Multiple Start Annealing (MSSA) metaheuristics were also evaluated, but the results of GRASP were the best. The results indicate that the proposed CLR approach is more effective than the linear and nonlinear algorithms mentioned in the literature, such as multiple lasso linear regression, random forests, XGBoost, and support vector machines. These algorithms achieved an accuracy of 70%. However, with the new CLR model, a significantly improved accuracy of 87% was achieved with test data. The clusters’ studies revealed key factors affecting crop yield, such as fertilization, drainage, and soil type. This transparency is a benefit over black-box models, which can be harder to interpret. This advancement can allow farmers to make better decisions about the management of their crops.

Evaluating the Patterns of Maize Development in the Hetao Irrigation Region Using the Sentinel-1 GRD SAR Bipolar Descriptor

Assessing maize yield is critical, as it is directly influenced by the crop’s growth conditions. Therefore, real-time monitoring of maize growth is necessary. Regular monitoring of maize growth indicators is essential for optimizing irrigation management and evaluating agricultural yield. However, quantifying the physical aspects of regional crop development using time-series data is a challenging task. This research was conducted at the Dengkou Experimental Station in the Hetao irrigation area, Northwest China, to develop a monitoring tool for regional maize growth parameters. The tool aimed to establish a correlation between satellite-based physical data and actual crop growth on the ground. This study utilized dual-polarization Sentinel-1A GRD SAR data, accessible via the Google Earth Engine (GEE) cloud platform. Three polarization descriptors were introduced: θc (pseudo-scattering type parameter), Hc (pseudo-scattering entropy parameter), and mc (co-polar purity parameter). Using an unsupervised clustering framework, the maize-growing area was classified into several scattering mechanism groups, and the growth characteristics of the maize crop were analyzed. The results showed that throughout the maize development cycle, the parameters θc, Hc, and mc varied within the ranges of 26.82° to 42.13°, 0.48 to 0.89, and 0.32 to 0.85, respectively. During the leaf development stage, approximately 80% of the maize sampling points were concentrated in the low-to-moderate entropy scattering zone. As the plants reached the big trumpet stage, the entire cluster shifted to the high-entropy vegetation scattering zone. Finally, at maturity, over 60% of the sampling points were located in the high-entropy distribution scattering zone. This study presents an advanced analytical tool for crop management and yield estimation by utilizing precise and high-resolution spatial and temporal data on crop growth dynamics. The tool enhances the accuracy of crop growth management across different spatial and temporal conditions.

Bacillus amyloliquefaciens MZ895491: an endophytic bacterium as potential biocontrol agent in integrated pest management of fall armyworm in maize

ABSTRACT The fall armyworm, Spodoptera frugiperda is a major pest of maize crops and necessitates effective control measures. Excessive use of chemical pesticides often results in the development of resistant insect populations and environmental contamination, highlighting the need for environmentally friendly substitutes. This research aimed to estimate the potential of maize (COH6) apoplastic fluid bacterium Bacillus amyloliquefaciens (MZ895491), as a biocontrol agent against second instar S. frugiperda larvae under laboratory and pot culture conditions. The results suggest that the presence of B. amyloliquefaciens in the artificial diet could reduce the growth and development of S. frugiperda larvae. Metabolic profiling of extracellular components of B. amyloliquefaciens revealed the presence of numerous defensive and anti-feeding metabolites. A study conducted in pots demonstrated that foliar application of B. amyloliquefaciens to 5 d old maize resulted in a significant increase in antioxidant activity (83.3%), chlorophyll content (63.3%) and phenolic content (81.3%) in the leaves of 35 d old maize after 24 h of S. frugiperda infestation. Feeding bioassay further indicated significant reduction in the feeding capacity of S. frugiperda larvae on maize inoculated with B. amyloliquefaciens. The study observed that a metabolic modification in maize leaves likely led to reduced growth in larvae during the whole plant bioassay. This suggests that foliar application of B. amyloliquefaciens may effectively serve as one of the biocontrol agents against Spodoptera frugiperda. The application appears to alter the feeding behaviour of the larvae and modify maize leaf metabolism.

Improving maize yield and drought tolerance in field conditions through activated biochar application

Amidst depleting water resources, rising crop water needs, changing climates, and soil fertility decline from inorganic modifications of soil, the need for sustainable agricultural solutions has been more pressing. The experimental work aimed to inspect the potential of organically activated biochar in improving soil physicochemical and nutrient status as well as improving biochemical and physiological processes, and optimizing yield-related attributes under optimal and deficit irrigation conditions. Biochar enhances soil structure, water retention, and nutrient availability, while improving plant nutrient uptake and drought resilience. The field experiment with maize crop was conducted in Hardaas Pur (32°38.37’N, 74°9.00’E), Gujrat, Pakistan. The experiment involved the use of DK-9108, DK-6321, and Sarhaab maize hybrid seeds, with five moisture levels of evapotranspiration (100% ETC, 80% ETC, 70% ETC, 60% ETC, and 50% ETC) maintained throughout the crop seasons. Furthermore, activated biochar was applied at three levels: 0 tons/ha (no biochar), 5 tons per hectare, and 10 tons per hectare. The study’s findings revealed significant improvements in soil organic matter, bulk density, nutrient profile and total porosity with biochar supplementation in soil. Maize plants grown under lower levels of ETC in biochar supplemented soil had enhanced membrane stability index (1.6 times higher) increased protein content (1.4 times higher), reduced malondialdehyde levels (0.7 times lower), improved antioxidant enzyme activity (1.3 times more SOD and POD activity, and 1.2 times more CAT activity), improved relative growth (1.05 times more) and enhanced yield parameters (26% more grain and stover yield, 16% more 1000-seed weight, 29% more total seed weight, 33% more apparent water productivity) than control. Additionally, among the two biochar application levels tested, the 5 tons/ha dose demonstrated superior efficiency compared to the 10 tons/ha biochar dose.

Effects of Different Fertilization Measures on Bacterial Community Structure in Seed Production Corn Fields

Different fertilization measures affect the soil’s physical and chemical properties and bacterial community structure, which in turn affects the growth environment and yield of maize seed production. Therefore, rational fertilization measures are important in maintaining and improving soil fertility and promoting maize crop growth. Research on fertilization practices in maize fields for seed production can help to increase agricultural productivity while protecting and enhancing soil health and achieving sustainable agricultural development. To clarify the effects of different fertilization measures on soil bacterial communities in seed corn fields, 16S rRNA high-throughput sequencing technology and PICRUSt method were used to explore the soil under different fertilization measures (CK as control, effects of single application of liquid organic fertilizer (M), single application of bacterial agents (BF), and combined application of liquid organic fertilizer and bacterial agents (M + BF)) on soil bacterial community structure characteristics and ecological functions. Proteobacteria (20.14–25.30%), Actinobacteriota (18.21–20.47%), Actinobacteriota (13.55–22.00%), and Chloroflexi (14.24–17.59%) were the dominant phyla. Bacillus, RB41, Arthrobacter, and Sphingomonas were the dominant genera. M + BF treatment significantly increased the relative abundance of Planctomycetota. The relative abundance of Bacillus and PaeniBacillus in M treatment decreased considerably, while the relative abundance of Turicibacter increased significantly. The relative abundance of Sphingomonas was reduced considerably in M and M + BF treatments. The relative abundance of Subgroup 10 decreased significantly after BF and M + BF treatments. BF treatment significantly increased the relative abundance of Skermanella. Redundancy analysis showed that alkali-hydrolyzed nitrogen (p = 0.044) was the main environmental factor affecting soil bacterial communities under different fertilizer treatments. PICRUSt function prediction results showed that metabolism was the main functional component of bacteria, accounting for 78.45–78.94%. The abundance of functional genes for terpenoid and polyketone metabolism, the endocrine system, the excretory system, and the immune system of the soil bacterial community was significantly increased under M treatment, while the abundance of functional genes for the digestive system was decreased considerably. Different fertilizer cultivation measures changed soil bacterial community composition and ecological function in maize fields. These results can provide a theoretical reference for the study of bacterial community succession characteristics in maize fields and the determination of appropriate fertilizer cultivation measures.

Potassium limits productivity in intensive cereal cropping systems in Southeast Asia.

Potassium (K) has received little attention as a potential yield-limiting factor in cropping systems. Here we investigated the K status in intensive cereal cropping systems in Indonesia, which are representative of many other Southeast Asian countries. Our analysis included nutrient input-output balance, leaf nutrient status, long- and short-term fertilizer trials, and farmer surveys. We revealed that soil K levels alone are insufficient to meet plant requirements, and current fertilizer applications are inadequate to prevent K deficiencies and large negative annual soil K balances in farmer fields (average -62 kgK ha-1). On-farm fertilizer trials indicated that nearly 80% of rice crops and 70% of maize crops achieved higher yields with the application of K fertilizer. Addressing K limitations will require an enhanced capacity to predict crop responses to K fertilizer, together with long-term, flexible fertilizer and crop residue management strategies. Furthermore, similar K limitations have probably emerged in other regions globally due to intensive cropping with insufficient K replenishment, which must be addressed to close yield gaps on existing farmland.

Maize crop performance on different Agricultural Production Systems and co-inoculation with A. brasilense and T. asperellum

Considering the increasing global population and demand for food, it is crucial to adopt agricultural practices that ensure both food security and environmental preservation. In this context, plant growth-promoting microorganisms (PGPM) and integrated crop-livestock-forestry systems (ICLF) have emerged as promising strategies for sustainable productive intensification. This research aimed to determine the effects of inoculating Azospirillum brasilense and Trichoderma asperellum on maize crops (Zea mays L.) grown in monocropping and ICLF systems. A complete randomized block design was performed in a 4 × 2 factorial arrangement with three replications. The crop systems included maize monocrop (C), crop-livestock (CL), crop-forest (CF), and crop-livestock-forest (CLF). Concomitantly, the co-inoculation (A. brasilense + T. asperellum) was compared to the control management (with no PGPM). CL and C systems with co-inoculation showed the highest yields, while the CF and CLF systems, which include the arboreal component, showed lower yields because of tree shading. CL + co-inoculation treatment was the most significant with increases of 17.14% in shoot dry mass, 43% in 1000-grain weight, and 9.56% in yield (t ha-1) compared to the C + control treatment. Additionally, the CL + co-inoculation treatment overcame the CLF + control by 136.63% in yield. Results indicate that the PGPM co-inoculation in maize within the CL system is an effective strategy for optimizing crop production.

Potato and Maize Plant Disease Detection Using Leaf Images

Plant diseases represent a serious threat to national productivity and global food security. Effective therapy for multiple diseases requires a precise and useful differentiation of them. In this work, a computerized system for the identification and categorization of diseases in potato and maize crops is developed using convolutional neural networks. The demonstration was created with the ResNet50V2 model and tested on a combined collection of images of leaves. The system achieved an astounding accuracy of 85.19. Enhancing model execution through exchange learning, fine-tuning, and information augmentation were all part of the process. With the use of another dataset, the trained model was verified and produced positive results, almost exactly differentiating between the disease-causing leaf type (potato or maize). This technology helps ranchers adopt sustainable and knowledgeable disease management methods by promoting timely mediations, which in turn advances disease discovery.

Enhancing Water Use Efficiency and Carbon Profitability Through the Long-Term Impact of Sustainable Farming Systems

This study aims to enhance water use efficiency, maximize productivity, and minimize environmental impact through the implementation of sustainable agricultural systems using drip irrigation systems. It investigates the effects of biodynamic farming compared to those of organic and conventional methods over a six-year period and focuses on soil properties, water use efficiency, crop yield, and environmental and economic perspectives. Using a biodynamic farming system resulted in an average increase in water use efficiency of 1.96 and 10.67% for maize and 3.62 and 10.68% for faba bean and an increase in maize yield of 1.68 and 0.99%, while the faba bean yield reached 3.25 and 1.57% compared to the organic and conventional farming systems, respectively. The biodynamic system sequestered the highest average soil carbon of 6.16 tons/ha (which is equivalent to 22.45 tons/ha of CO2 emissions), representing a 13% increase compared to the organic system. Additionally, the biodynamic system yielded an increase in total net profit of 5.70 and 21.66% for the maize crop and 6.72 and 22.19% for the faba bean crop compared to the organic and conventional farming systems, respectively. The farming system significantly influenced the soil carbon sequestration and organic carbon.

GRASSIUS 2.0: A gene regulatory information knowledgebase for maize and other grasses

Grass species, which include the major cereal crops maize, wheat, rice, and sugarcane, are an integral part of our global agriculture and source of food and energy for a growing world population. GRASSIUS was established as a knowledgebase for transcription factors (TFs) and coregulators (CoRegs) in maize and several other species in the grass family. TFs are a primary component of the gene regulatory networks (GRNs) and the underlying gene regulatory grids (GRGs) that govern all aspects of plant growth and metabolism. GRASSIUS is the source for all information pertaining to the maize TFome collection, which serves as a powerful resource for the discovery of GRNs in maize and other cereals [1,2]. Here we describe the release of the GRASSIUS 2.0 knowledgebase (www.grassius.org) with updated data, query, and tool features, as well as the ability to expand to accommodate future datasets. The membership and annotation of all TF and CoReg families has been updated and revised to include gene models from v3, v4, and v5, of the maize B73 genome and recent genome versions of rice, sorghum, Brachypodium, and sugarcane. A translation tool enables cross referencing of Gene IDs between versions of the maize genome. Protein-DNA interactions (PDIs) have been added incorporating results derived from various gene- and TF-centered PDI discovery tools and visualized through a new web-based interface. A filtering tools permits the selection and visualization of PDIs within a ± 2 kb distance from the transcriptional start site (TSS) of a gene of interest. A new BLAST (Basic Local Alignment Search Tool) tool facilitates searching of the maize TFome as well as v3, v4, and v5 TF gene model sets. Lastly, we describe the methodology used to implement GRASSIUS 2.0 which can guide others in developing and updating similar plant gene regulatory knowledgebases.

Improved Agronomic Practices on Enhancement of Quality Forage Production for Livestock Farming

The lack of nutrient-rich fodder and forage is one of the problems of livestock farming. Adequate tillage operations, timely and suitable water management, weed management, insect pests and disease management, fertilizer management, seeding at the proper time and seed rate, timely harvesting, and other agronomic techniques can all help to increase the nutritional content and yield of fodder and forage crops. A number of research and review papers have been systematically reviewed in this study. The use of tillage practices such as primary, secondary, conventional, and deep tillage enhances dry matter and yield of green fodder as compared to zero tillage. The organic matter (OM) content and dry matter (DM) of fodder crops are increased by regular and appropriate irrigation. The DMD (dry matter digestibility) and CP (crude protein) contents of early harvested forages are higher than those of late harvested forages. The application of nitrogen promotes crop development and growth, increasing the production of green fodder and improving its quality. Intercropping is essential for increasing the yield of fodder crops. Compared to sole cropping of maize and cowpea, the yield is found higher in intercropping of maize + cowpea. The production and quality of fodder are decreased by late seeding. The management of insect pests and diseases enhances the production and quality of fodder and forage. Thus, we draw the conclusion that fodder and forage crops production and their quality parameters are greatly influenced by agronomic practices.

The effect of aeration and irrigation on the improvement of soil environment and yield in dryland maize.

The aim of this study was to examine the effect of long-term aerated seepage irrigation technology on soil fertility changes and maize yield under continuous maize cropping system in red loam soil, and to explain the mechanism of maize yield increase under this technology, which can provide theoretical basis for crop quality improvement and yield increase under aerated irrigation (AI) technology. Therefore, this research was conducted for four field seasons in 2020-2023 at the National Soil Quality Observation Experimental Station, Zhanjiang, China. Soil aeration, soil fertility, root growth, physiological traits, and yield indicators were evaluated by conventional underground drip irrigation (CK) and AI. Our results showed that AI treatment significantly improved soil aeration and soil fertility. Increases in soil oxygen content, soil respiration rate, soil bacterial biomass, and soil urease activity were observed, corresponding to increases from 3.08% to 21.34%, 1.90% to 24.71%, 26.37% to 0.09%, and 12.35% to 100.96%, respectively. The effect of AI on maize indicators increased year by year. Based on improvements in soil aeration and fertility, root length, root surface area, and root dry weight under AI treatment were enhanced by 15.56% to 53.79%, 30.13% to 62.31%, and 19.23% to 35.64% (p < 0.05) compared to the CK group. In addition, maize agronomic traits and physiological characteristics showed improved performance; in particular, over 1.16% to 14.42% increases were identified in maize yield by AI treatment. Further analysis using a structural equation model (SEM) demonstrated that the AI technology significantly promotes the improvement of root indicators by enhancing soil aeration and soil fertility. As a result, maize yield could be increased significantly and indirectly.

The Application of a Foliar Spray Containing Methylobacterium symbioticum Had a Limited Effect on Crop Yield and Nitrogen Recovery in Field and Pot-Grown Maize.

In this study, the effectiveness of an inoculant containing a nitrogen (N)-fixing microorganism (Methylobacterium symbioticum) was evaluated on maize (Zea mays L.) grown both in the field (silage maize) and in pots over two years (2021 and 2022). The field trial included the following two treatments: with (Yes) and without (No) the inoculant. The pot experiment was designed as a factorial arrangement with two factors: the application of the inoculant (Yes and No) and N applied to the soil (0, 0.4, 0.8, and 1.6 g pot-1). In the field, total dry matter yield (DMY) did not differ significantly between treatments, although the average DMY was higher in the inoculant treatment. In pots, the total DMY varied significantly across all N rates but was only significantly affected by the inoculant application in 2022. N fixation estimates in the field were 58.8 and 14.5 kg ha-1 for 2021 and 2022, respectively, representing 23.7% and 9.1% of the N recovered in the aboveground plant parts. In pots, the estimated fixed N values were -49.2 and 199.2 mg pot-1 in 2021 and 2022, respectively, which corresponded to -5.2% and 18.5% of the N found in the aboveground plant parts. Considering the average values obtained across the four cultivation conditions, there was a positive outcome for the treated plants. However, these values cannot be considered significant when compared to nitrogen removal in maize crops. A commercial product should provide an unequivocal and quantitatively relevant contribution to plant nutrition, which did not appear to be the case. Thus, for this inoculant to provide reliable guarantees of positive outcomes for farmers and become a useful tool in promoting more sustainable agriculture, further studies appear necessary. These studies should aim to determine in which crops and under what cultivation conditions the application of the inoculant is truly effective in enhancing N fixation and improving crop productivity.