Maize Yield Research Articles

Cytogenetic impact of gamma radiation and its effects on growth, yield and drought tolerance of maize (Zea mays L.)

Abstract Maize is the third most important grain crop worldwide after wheat and rice; it is a vital global crop, serving as a key source of food, animal feed, and industrial products, making it essential for food security and economic stability in many countries. Drought stress adversely affects water uptake and can stunt growth, reducing the overall productivity of maize. So, this study was carried out to investigate the cytogenetic effects of gamma radiation and drought stress on maize SC131 genotype, focusing on chromosomal aberrations in seedling root meristems induced by varying doses of gamma irradiation (50, 100, 150, 200, and 250 Gray) and drought stress imposed by 10% polyethylene glycol (PEG). The present study also aims to evaluate the impact of these treatments on growth parameters under a controlled pot experiment. Additionally, molecular polymorphism induced by both gamma irradiation and drought stress was analyzed using Real-Time quantitative PCR techniques for DREB2, ERF, and EF transcription factors. Also, under a field condition experiment, maize plants were subjected to the same gamma irradiation doses and drought stress by reducing the number of irrigations, with subsequent evaluations of yield attributes to assess the overall impact of treatments on plant performance. The study also investigates the sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) banding patterns of proteins in grains yielded under the influence of gamma radiation and drought treatments. Findings of the current investigation indicate that the low dose of gamma radiation (50 Gray) not only induces cytogenetic changes but also enhances drought tolerance and improves yield characteristics, suggesting that targeted gamma irradiation could serve as a viable strategy to bolster maize resilience in challenging environmental conditions.

Novel microbial technologies for optimizing maize plant-soil systems: the multifunctional strain Enterobacter sp. ES1.

In recent years, a new microbial technology has emerged to optimize maize field productivity and soil health. This is crucial because of the adverse effects of pesticide contamination and diseases on maize yields. Enterobacter sp. ES1, a multifunctional strain, effectively degraded 93.09% of nicosulfuron within 4 days. Optimal degradation occurred at 35 °C and pH 6.0, with 4% inoculum and 20 mg L-1 nicosulfuron. Strain ES1 could fix nitrogen and solubilize phosphorus and potassium. It also detoxified potassium and produced chrome azurol S and indole-3-acetic acid. Moreover, it promoted rapid maize seed germination, induced resistance to northern maize leaf blight, and up-regulated maize genes ZmOPR2, ZmPR5, ZmHPL, ZmOX10, ZmPAL, ZmPR1, ZmAOS, and ZmCTR1. Strain ES1R-gfp, prepared using green fluorescent protein and antibiotic domestication methods, functioned as a bacterial fertilizer with 31.04% sawdust, 32.96% straw, and 12.15% biochar, exhibiting a degradation rate of 99.65%. The novel microbial technology of the multifunctional strain ES1 was verified through a pot experiment assessing nicosulfuron concentration and plant and soil indices, thereby providing a foundation for optimizing maize field restoration. © 2025 Society of Chemical Industry.

Using Klebsiella sp. and Pseudomonas sp. to Study the Mechanism of Improving Maize Seedling Growth Under Saline Stress

The increasing salinization of cultivated soil worldwide has led to a significant reduction in maize production. Using saline–alkaline-tolerant growth-promoting bacteria (PGPR) in the rhizosphere can significantly improve the saline tolerance of maize and ensure the stability of maize yields, which has become a global research hotspot. This study screened salt-tolerant microorganisms Klebsiella sp. (GF2) and Pseudomonas sp. (GF7) from saline soil to clarify the mechanism in improving the saline tolerance of maize. In this study, different application treatments (GF2, GF7, and GF2 + GF7) and no application (CK) were set up to explore the potential ecological relationships between the saline tolerance of maize seedlings, soil characteristics, and microorganisms. The results showed that co-occurrence network and Zi-Pi analysis identified Klebsiella and Pseudomonas as core microbial communities in the rhizosphere soil of maize seedlings grown in saline soil. The deterministic process of microbial assembly mainly controlled the bacterial community, whereas bacteria and fungi were governed by random processes. The application of saline–alkaline-resistant PGPR under saline stress significantly promoted maize seedling growth, increased the activity of soil growth-promoting enzymes, and enhanced total nitrogen, soil organic carbon, and microbial carbon and nitrogen contents. Additionally, it reduced soil salt and alkali ion concentrations [electrical conductivity (EC) and exchangeable Na+]. Among them, GF2 + GF7 treatment had the best effect, indicating that saline–alkaline-tolerant PGPR could directly or indirectly improve the saline tolerance of maize seedlings by improving the rhizosphere soil ecological environment. EC was the determining factor to promote maize seedling growth under saline–alkaline stress (5.56%; p < 0.01). The results provided an important theoretical reference that deciphers the role of soil factors and microecology in enhancing the saline tolerance of maize.

Effect of One-Time Application of Biochar and Compost on Soil and Maize During 5-Time Consecutive Periods of Crop Cultivation

This study evaluates the impact of a single-time biochar application during initial cultivation on the performance of five consecutive crop cycles. The research compares the effects of biochar alone versus biochar combined with soybean compost on maize yield and soil properties over a period of 2.8 years. Fundamental soil properties—including pH, cation exchange capacity, organic matter content, and macronutrient levels—were assessed before each planting cycle and at the end of the fifth cycle. Maize yield and productivity were evaluated based on the number of maize ears, kernel biomass, and both fresh and dry kernel weights. Five experimental plots, each with four replicates, were established with the following treatments: compost applied at 0.56 kg/sq m (TM), cassava stem (CS) biochar applied alone at 2.5 kg/sq m (TB2.5) and 3.0 kg/sq m (TB3.0), and combinations of compost at 0.56 kg/sq m with CS biochar at 2.5 kg/sq m (TMB2.5) and 3.0 kg/sq m (TMB3.0). Results indicated that the sole application of biochar and its combination with compost positively affected soil properties and maize yield. Biochar applications alone significantly improved soil nutrient levels and maize yields compared to the compost alone. Notably, the beneficial effects of biochar on maize and soil were observed from the first cultivation and persisted throughout all five cycles. Based on these findings, it is recommended to apply biochar at 3.0 kg/sq m, in combination with compost at 0.56 kg/sq m, every three crop cycles to sustain nutrient levels and enhance maize yields effectively. Doi: 10.28991/ESJ-2025-09-01-07 Full Text: PDF

Grain yield and water productivity of maize under deficit irrigation and salt stress: Evidences from field experiment and literatures

Grain yield and water productivity of maize under deficit irrigation and salt stress: Evidences from field experiment and literatures

Differences in effects of varying compound extreme temperature and precipitation events on summer maize yield in North China

Differences in effects of varying compound extreme temperature and precipitation events on summer maize yield in North China

Influence of Time of Weed Removal on Maize Yield and Yield Components Based on Different Planting Patterns, the Application of Pre-Emergence Herbicides and Weather Conditions

Influence of Time of Weed Removal on Maize Yield and Yield Components Based on Different Planting Patterns, the Application of Pre-Emergence Herbicides and Weather Conditions

Determination of the effects of different sowing times and densities under ıı. product conditions on the yield and components of maize varieties belonging to two different maturity groups

This study was conducted using maize (Zea mays L.) varieties from two different maturity groups (DKC5747 (FAO 500) and PR31P41 (FAO 650)) under II—product conditions. The effects of different sowing times (June 1, June 20, July 10) and planting densities (800, 900, 1000, and 1100 plants per hectare) on these varieties' yield and yield components were investigated. The objective was to determine the optimal maturity group, sowing time, and planting density for achieving the highest yield in maize (Zea mays L.). The features analyzed included plant height (cm), cob length (cm), cob weight (g/cob), corn grain number per cob (corn grains/cob), single corn grain weight (mg/corn grain), and grain yield per cob (g/cob). The results showed that sowing time significantly influenced all features examined. The highest values for cob length, weight, corn grain number per cob, and grain yield per cob were obtained at the second sowing time (June 20), while the lowest values were recorded at the third sowing time (July 10). The optimal planting density was determined to be 900–1100 plants/ha for the DKC5747 variety and 800 plants/ha for the PR31P41 variety. For the Amik Plain, the most suitable sowing time for second-crop maize cultivation was June 20, with an optimal planting density of 900–1100 plants/da. The most appropriate varieties were those in the short maturity group. In maize cultivation as a second crop under the conditions of the Amik Plain in Hatay, it is crucial to prioritize the selection of maize varieties in the short maturity group (FAO 500 maturity group) to achieve high grain yields. When longer maturity varieties are selected, it should be noted that while the plants may produce higher biomass, their capacity to convert dry matter into grain may be insufficient, potentially leading to reduced grain yields.

Integrating Climate Data and Remote Sensing for Maize and Wheat Yield Modelling in Ethiopia’s Key Agricultural Region

Integrating Climate Data and Remote Sensing for Maize and Wheat Yield Modelling in Ethiopia’s Key Agricultural Region

Influence of Planting Systems and Nutrient Management on Maize Growth, Yield and Light Interception in a Maize-soybean Intercropping System

Influence of Planting Systems and Nutrient Management on Maize Growth, Yield and Light Interception in a Maize-soybean Intercropping System

Bio-inoculant consortium and organic amendment comprising plant bioactive extract increased maize yield by improving soil nutrient availability and mitigating pest damage

Bio-inoculant consortium and organic amendment comprising plant bioactive extract increased maize yield by improving soil nutrient availability and mitigating pest damage

Combined Mineral and Organic Fertilizer Application Enhances Soil Organic Carbon and Maize Yield in Semi-Arid Kenya: A DNDC Model-Based Prediction

The application of mineral fertilizers can effectively enhance crop yields. However, this potential benefit may be diminished if the use of mineral fertilizers leads to a substantial decline in soil organic carbon (SOC) and an increase in soil greenhouse gas (GHG) emissions. This study aimed to determine the optimal fertilizer combinations and rates for improving SOC and maize yield while reducing GHG emissions in the semi-arid uplands of Kenya. Data were collected from five different fertilizer treatments (N50, N100, N150, N100+manure, and N100+straw) compared to a control (N0) in a long-term experimental field, which was used to run and validate the DNDC model before using it for long-term predictions. The results showed that the combination of mineral fertilizer and straw resulted in the highest SOC balance, followed by that of fertilizer and manure. All fertilized treatments had higher maize grain yields compared to low-fertilizer treatment (N50) and control (N0). Daily CO2 fluxes were highest in the treatment combining mineral fertilizer and manure, whereas there were no significant differences in N2O fluxes among the three tested treatments. The findings of this study indicate that the judicious application of mineral fertilizer, animal manure, and straw has great potential in enhancing SOC and maize yields while reducing GHG emissions, thereby providing practical farming management strategies in semi-arid Kenya.

Effect of foliar application of nano micro nutrients and deficit irrigation on stay green characteristics and drought resistance in maize

The stay-green character is a crucial trait linked to delayed leaf senescence, which enables the plant to continue photosynthetic activity for an extended time under early and terminal drought. Reduced water availability causes early leaf senescence, lower chlorophyll content and eventually poor yield in maize. The objectives were to quantify the effects of irrigation regimes, nanocomposite levels physiological parameters, yield attributes and yield of maize. The main plot treatments comprised of well irrigated and withheld irrigation, while the sub plot treatments consisted of different nanoparticles viz., ZnO, MnO, (ZnO + MnO), TNAU nano revive and ZnSO4 + MnSO4. The results revealed that higher dry matter production (5282 and 9891 kg/ha), leaf nitrogen (44.28 and 39.97), grain filling rate, grain filling duration (34.44 days), green leaf area (92.50%) and proline content (0.59 and 1.28 mg g -1) were recorded at tasseling and grain filling stage, respectively under well-irrigated conditions. Foliar spraying of ZnO (100 ppm) and MnO (20 ppm) nanocomposite, registered higher root biomass (24.21 and 32.11 g/plant), leaf nitrogen (44.6 and 39.1), dry matter production, green leaf area, lowest proline content which ultimately resulted in a higher number of cobs/plant, number of grains row/cob, number of grains/grain row, test weight (1000 grains weight), shelling percentage, crop water use (18.79 kg/ha/mm), grain yield (8.20 t/ha), stover yield (12.2 t/ha) and benefit cost ratio of 2.4. Thus, it could be concluded that well-irrigated condition followed by foliar spray with ZnO (100 ppm) and MnO (20 ppm) registered higher growth, yield attributes, yield, and economics.

Assessing Yield and Productivity Gaps in Tunisian Maize Cropping System

Maize production is deficient in arid countries such as Tunisia. To assess maize yield and estimate productivity gaps among Tunisian farmers in consideration of climate change challenges, a survey was conducted that included 50 farms in 10 governorates, focusing on agronomic practices, seed type adoptions, and socioeconomic parameters. The yield gaps related to water resources and farmers’ technical efficiency represented 26.8% and 32.9%, respectively, while for water productivity, the gaps related to water resources and technical efficiency were 32.2% and 31.3%, respectively. Hybrid varieties were among the 25% yield increase compared to local landraces. Farmers retain local landraces mainly for their food quality. Favorable climatic conditions in the northern regions of Tunisia are among the reasons for higher yield compared to the central and southern areas, which registered a yield reduction of 9.2% and 17%, respectively. The Tobit analyses showed that sowing rate, geographic location, type of variety, and fertilization are the most significant factors contributing to technical inefficiencies. For further increases in maize yield in Tunisia, improving agricultural practices, water management, and using high-yielding varieties are essential.

Integrated analysis of transcriptome, sRNAome, and degradome involved in the drought-response of maize Zhengdan958.

Drought is a major abiotic stress in restricting the growth, development, and yield of maize. As a significant epigenetic regulator, small RNA also functions in connecting the transcriptional and post-transcriptional regulatory network. Further to help comprehending the molecular mechanisms underlying drought adaptability and tolerance of maize, an integrated multi-omics analysis of transcriptome, sRNAome, and degradome was performed on the seedling roots of an elite hybrid Zhengdan958 under drought stress. In this study, 2,911 genes, 32 conserved miRNAs, and 12 novel miRNAs showed a significantly differential expression under drought stress. Moreover, 6,340 target genes of 445 miRNAs were validated using degradome sequencing, forming 281 miRNA-mRNA pairs in control (CK) and drought-stressed (DS) library. These target genes were mainly involved in the plant hormone signal transduction and phenylpropanoid biosynthesis pathways. The integrated multi-omics analysis revealed that five DEmiRNA-mRNA pairs displayed negatively correlated expression patterns, which were also verified by qRT-PCR. Tissue-specific expression profile and regulatory network analysis revealed that miR528a/b-Zm00001d021850, miR408a/b-Zm00001d020794, and miR164e-Zm00001d003414 might be essential in root-specific drought stress response of maize Zhengdan958 seedlings. These worthwhile will promote the functional characterization of miRNA-mRNA modules response to drought stress, and potentially contribute to drought-resistance breeding of maize.

Effects of Organic and Inorganic Fertilizers on Soil Properties, Nutrient Dynamics, and Maize Yield (Zea mays L.)

A field experiment was carried out during the winter seasons of 2021-22 and 2022-23 at the Zonal Agriculture Research Station at Chhindwara. The objective of the experiment was to investigate the impact of various organic and inorganic fertilizers on the nutrient content (N, P, K & Zn), uptake and yield of maize. The experiment was structured using a randomized block design, incorporating ten treatments and three replications. The treatments were, T1 - Control (0:0:0), T2 - 100% RDF (120:60:40 kg NPK ha-1), T3 - 75% RDF, T4 - 50 % RDF, T5 - FYM 10 t ha-1 + Azotobacter, T6 - 100 % RDF + 5 t ha-1 FYM, T7 - 75 % RDF + 5 t ha-1 FYM, T8 - 50 % RDF + 5 t ha-1 FYM, T9 - 100 % RDF + 5 kg Zn ha-1 and T10 - FYM 5 t ha-1 (State practice). The result revealed that the application of 100 % RDF + 5 t ha-1 FYM (T6) recorded higher values of soil chemical properties viz., pH (7.167), EC (0.329), OC (0.350), available nitrogen (340.50), phosphorus (15.92) and potassium (320.26), nitrogen content in grain (1.638) and stover (0.482), phosphorus content in grain (0.407) and stover (0.040), potassium content in grain (0.554) and stover (1.636), nitrogen uptake (196.347), phosphorus uptake (36.107), potassium uptake (275.869), Zn content in grain (30.460 ppm) and stover (21.242 ppm), grain yield (7957.53 kg ha-1) and stover yield (14473.65 kg ha-1).

Instar identification and weight prediction of Ostrinia furnacalis (Guenée) larvae using machine learning.

The Asian corn borer, Ostrinia furnacalis (Guenée), emerges as a significant threat to maize cultivation, inflicting substantial damage upon the crops. Particularly, its larval stage represents a critical point characterised by significant economic consequences on maize yield. To manage the infestation of this pest effectively, timely and precise identification of its larval stages is required. Currently, the absence of techniques capable of addressing this urgent need poses a formidable challenge to agricultural practitioners. To mitigate this issue, the current study aims to establish models conducive to the identification of larval stages. Furthermore, this study aims to devise predictive models for estimating larval weights, thereby enhancing the precision and efficacy of pest management strategies. For this, 9 classification and 11 regression models were established using four feature datasets based on the following features geometry, colour, and texture. Effectiveness of the models was determined by comparing metrics such as accuracy, precision, recall, F1-score, coefficient of determination, root mean squared error, mean absolute error, and mean absolute percentage error. Furthermore, Shapley Additive exPlanations analysis was employed to analyse the importance of features. Our results revealed that for instar identification, the DecisionTreeClassifier model exhibited the best performance with an accuracy of 84%. For larval weight, the SupportVectorRegressor model performed best with R2 of 0.9742. Overall, these findings present a novel and accurate approach to identify instar and predict the weight of O. furnacalis larvae, offering valuable insights for the implementation of management strategies against this key pest.

Impact of two organic fertilizers (mycorhize and poultry manure) associated with pollinators insects on growth and yields of Zea mays L. at Baré (Littorale Region-Cameroon)

Field of maize was conducted during the rainy season in 2021 and 2022 in Baré (Littorale Region - Cameroon) aiming to contribute to the improvement of the yield of maize by combining two organic fertilizers and pollinators insects. The main factor was pollination with two levels (plants left on free pollination and plants isolated from insects). The secondary aspect was fertilization with four points, mycorhize-poultry manure, mycorhize, poultry manure and control. The growth parameters, the diversity and foraging activity of pollinators insects, the influence of organic fertilizers and pollinator insects were evaluated on the yields. The results showed that the plants located on the plots treated with mycorhize-poultry, mycorhize and poultry manure showed significant growth on the number of the leaves (p<0.05) and collar diameter (p<0.05). In both years, a total of four orders of pollinator insects have been identified where Hymenoptera (90.94%) ranks first followed by Diptera (8.71%). Those insects harvested pollen (100%). Mean number of seeds per ears and the normal seeds left on free pollination was greater compared to those isolated from insects (p<0.001). The production (normal seeds and the mean number of seeds per ears) varies to 36.41 to 41.72 % in the plots having mycorhize-poultry manure, to 32.94 to 38.32 % plots having mycorhize and to 30.89 to 31.06 % to poultry manure plots associated with pollinators insect respectively. The use of organic manure especially the mycorhize-poultry manure and the maintenance of pollinators insect nest around the field mays by recommended to improve crop yield.

Research on Mass Prediction of Maize Kernel Based on Machine Vision and Machine Learning Algorithm

The yield assessment process during maize harvesting is a necessary means to ensure farmers’ economic benefits and stable agricultural production. Predicting the mass of maize kernels is an important condition for yield detection. This study proposes a maize kernel mass prediction model based on machine vision and machine learning algorithms to determine whether the kernels are broken. By extracting the geometric features of maize kernels, a phenotypic feature dataset of maize kernels was constructed. Subsequently, popular machine learning algorithms were used to establish regression models for maize kernel mass, achieving quantitative prediction of maize kernel mass. The results indicate that the PLSR (Partial Least Squares Regression) and RF (Random Forest) algorithms are suitable for constructing mass prediction models for broken and unbroken kernels, respectively. The models established by the two algorithms achieved R-values of 0.941 and 0.925, respectively. Field trial results show that there is a strong linear relationship between the predicted maize kernel mass using the constructed model and the actual kernel mass. Therefore, this method can serve as an accurate, objective, and efficient detection method for maize yield.

Nitrogen Management Utilizing 4R Nutrient Stewardship: A Sustainable Strategy for Enhancing NUE, Reducing Maize Yield Gap and Increasing Farm Profitability

The imbalanced use of fertilizers, particularly the inefficient application of nitrogen (N), has led to reduced nitrogen use efficiency (NUE), lowered crop yields and increased N losses in Nepal. This study aimed to enhance yields, NUE and farm profitability by optimizing N fertilizer rates, application timing and methods through multilocation trials and demonstrations. In 2017, 57 field trials were conducted in two mid-hill districts using a completely randomized block design. The treatments included control (CK), NPK omission (N0, P0 and K0), variable N rates (60, 120, 180 and 210 kg N ha−1) and top-dressing timings (120 kg N ha−1 applied at knee height and shoulder height, V6, V10 and V8 stages). A full dose of recommended P (60 kg ha−1) and K (40 kg ha−1) were applied at planting, while N was top-dressed in two equal splits at knee-height and shoulder-height growth stages for P and K omission treatments, as well as for treatment with variable N rates. Grain yields responded quadratically, with optimum N rates ranging from 120 to 180 kg ha−1 across the districts. N applied at 120 kg ha−1 and top-dressed at V6 and V10 increased maize yield by 20–25%, partial factor productivity of nitrogen (PFPN) by 12%, agronomic efficiency of nitrogen (AEN) by 21% and gross margin by 10% compared to conventional knee and shoulder height application. In 2018 and 2019, fertilizer BMPs, including V6 and V10 top-dressing and the urea briquette deep placement (UDP) were demonstrated on 102 farmers’ fields across five mid-hill districts to compare their agronomic and economic significance over traditional farmers’ practice (FP). UDP, validated in 2018 field trials, increased yields by 34% (8.8 t ha−1) and urea top-dressing at V6 and V10 increased yield by 33% (8.7 t ha−1) compared to FP (5.8 t ha−1), reducing the average yield gap by 3.0 t ha−1. Moreover, the gross margin was increased by 39% (V6 and V10) and 40% (UDP) over FP. The findings highlight the need for widespread adoption of fertilizer BMPs to close the yield gap and maximize profitability with minimal nitrogen footprint.