Search Results (279)

Agronomic advancements have led to significant increases in maize yield per hectare in Northeast China, primarily through improved density tolerance. However, the genetic mechanism underlying grain yield responses to density stress remains poorly understood. Here, a population of 193 recombinant inbred lines (RILs) derived from the cross between ZM058 and PH1219 was employed to identify quantitative trait loci (QTLs) under two planting densities across three locations over two years. Six yield-related traits were investigated: ear tip-barrenness length (BEL), cob diameter (CD), ear diameter (ED), ear length (EL), kernel number per row (KNR), and kernel row number (KRN). These traits exhibited distinct and divergent responses to density stress, with the values of CD, ED, EL, KNR and KRN decreasing as planting density increased, except for BEL. A total of 81 QTLs were identified for these traits: 39 were unique to low planting density, 22 to high planting density, and 20 were shared across both conditions. Additionally, nine QTL clusters implicated in the development of multiple traits were detected. The results indicate that planting density significantly affects yield traits, primarily through the interaction of numerous minor QTLs with multiple effects. This insight enhances our understanding of the genetic basis of yield-related traits and provides valuable guidance for breeding high-density-tolerant varieties. Full article

Maize (Zea mays L.) is a key global cereal crop with significant relevance to food security. Maize yield prediction is challenged by cultivar diversity and varying management practices. This preliminary study was conducted at Youyi Farm, Heilongjiang Province, China. Three maize cultivars (Songyu 438, Dika 1220, Dika 2188), two fertilization rates (700 and 800 kg·ha⁻¹), and three planting densities (70,000, 75,000, and 80,000 plants·ha⁻¹) were evaluated across 18 distinct cropping treatments. During the V6 (Vegetative 6-leaf stage), VT (Tasseling stage), R3 (Milk stage), and R6 (Physiological maturity) growth stages of maize, multi-temporal canopy spectral images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor. In situ measurements of key agronomic traits, including plant height (PH), stem diameter (SD), leaf area index (LAI), and relative chlorophyll content (SPAD), were conducted. The optimal vegetation indices (VIs) and agronomic traits were selected for developing a maize yield prediction model using the random forest (RF) algorithm. Results showed the following: (1) Vegetation indices derived from the red-edge band, particularly the normalized difference red-edge index (NDRE), exhibited a strong correlation with maize yield (R = 0.664), especially during the tasseling to milk ripening stage; (2) The integration of LAI and SPAD with NDRE improved model performance, achieving an R² of 0.69—an increase of 23.2% compared to models based solely on VIs; (3) Incorporating SPAD values from middle-canopy leaves during the milk ripening stage further enhanced prediction accuracy (R² = 0.74, RMSE = 0.88 t·ha⁻¹), highlighting the value of vertical-scale physiological parameters in yield modeling. This study not only furnishes critical technical support for the application of UAV-based remote sensing in precision agriculture at the field-plot scale, but also charts a clear direction for the synergistic optimization of multi-dimensional agronomic traits and spectral features. Full article

Plant Growth Promoting Rhizobacteria, PGPR, can protect plants against soil-borne diseases and abiotic stress conditions. The primary objective of this study was to evaluate the effects of different PGPRs (TF1, TF2, TF3, and TF4) on the rhizosphere microbial community of silage maize in a saline–alkaline field via Illumina MiSeq high-throughput sequencing technology. Results demonstrated that different PGPRs significantly increased the harvest density (by 21.31–45.16%), plant height (by 9.12–19.98%), stem diameter (by 30.07–45.78%), and biomass (by 33.20–65.36%) of silage maize, TF3 treatment significantly increased the fresh weight (by 32.50%), while the other treatments could increase the fresh weight but not significantly. Four microbial agents significantly reduced the contents of soil available phosphorus (AP), electrical conductivity (EC), and neutral phosphatase activity (NPA), while significantly increasing the contents of available potassium (AK), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), chitinase activity (ChtA), and urease activity (UA). Specifically, TF2 and TF3 treatments significantly decreased the soil pH value, while not for TF1 and TF4. Microbiome analysis showed that four microbial agents significantly increased the relative abundances of beneficial microorganisms, such as Arthrobacter, Blastococcus, MNDI, Chaetomidium, Alternaria, Sarocladium, Acremonium, and Clonostachys, and significantly decreased the relative abundances of Gibberella and Fusarium. Mental analysis showed that the soil bacterial community structure did not significantly correlate with soil biochemical properties, while the soil fungal community structure significantly and positively correlated with pH. Maize yield significantly and positively correlated with NH₄⁺-N, OM, AP, EC, UA, ChtA, and NPA. Full article

Rapid and accurate identification of maize architecture characteristics is important for understanding both yield potential and crop breeding experiments. Most canopy architecture indicators cannot fully reflect the vertical leaf distribution in field environments. We conducted field experiments on sixty maize cultivars under four planting densities at three different sites, and herein introduce two novel indicators, “kurtosis and skewness,” based on the manually measured leaf area index (LAI) of maize at five different canopy heights. Then, we constructed the LAI, plant height (PH), kurtosis, and skewness estimation models based on unmanned aerial vehicle multispectral, RGB, and laser detecting and ranging data, and further assessed the canopy architecture and estimated yield. The results showed that the fitting coefficient of determination (R²) of cumulative LAI values reached above 0.97, and the R² of the four indicators’ estimation models based on multi-source data were all above 0.79. A high LAI, along with greater kurtosis and skewness, optimal PH levels, and strong stay-green ability, are essential characteristics of high-yield maize. Moreover, the four indicators demonstrated high accuracy in estimating yield, with the R² values based on measured canopy indicators at the four planting densities being 0.792, 0.779, 0.796, and 0.865, respectively. Similarly, the R² values for estimated yield based on estimated canopy indicators were 0.636, 0.688, 0.716, and 0.775, respectively. These findings provide novel insight into maize architecture characteristics that have potential application prospects for efficient estimation of maize yield and the breeding of ideal canopy architecture. Full article

Canopy coverage is a key indicator for judging maize growth and production prediction during the seedling stage. Researchers usually use deep learning methods to estimate canopy coverage from maize images, but fully supervised models usually need pixel-level annotations, which requires lots of manual labor. To overcome this problem, we propose ASLNet (Automatic Scribble Labeling-based Semantic Segmentation Network), a weakly supervised model for image semantic segmentation. We designed a module which could self-generate scribble labels for maize plants in an image. Accordingly, ASLNet was constructed using a collaborative mechanism composed of scribble label generation, pseudo-label guided training, and double-loss joint optimization. The cross-scale contrastive regularization can realize semantic segmentation without manual labels. We evaluated the model for label quality and segmentation accuracy. The results showed that ASLNet generated high-quality scribble labels with stable segmentation performance across different scribble densities. Compared to Scribble4All, ASLNet improved mIoU by 3.15% and outperformed fully and weakly supervised models by 6.6% and 15.28% in segmentation accuracy, respectively. Our works proved that ASLNet could be trained by pseudo-labels and offered a cost-effective approach for canopy coverage estimation at maize’s seedling stage. This research enables the early acquisition of corn growth conditions and the prediction of corn yield. Full article

Soil degradation limits maize grain yield, but the mechanisms by which leaf functions respond to topsoil depth and their contributions to yield are unclear. We quantified the response mechanisms of leaf functions to topsoil depth with topsoil depths of 10 cm (S₁), 20 cm (S₂), 30 cm (S₃), 40 cm (S₄), and 50 cm (S₅) and planting densities of 15,000 plants ha⁻¹ (D₁, the plant spacing was 111.1 cm and there was no mutual influence between individuals) and 75,000 plants ha⁻¹ (D₂). The grain yield in S₁ was significantly lower than that in S₂, S₃, S₄, and S₅, and the maximum reductions in yield were 39.7% in D₁ and 39.1% in D₂. The coefficients of variation for yield in S₁ and S₂ were significantly higher than those in S₃, S₄, and S₅ at both densities and in both years. The net assimilation rate and production efficiency of leaf area, as well as leaf nitrogen and carbon accumulation, all decreased with decreasing topsoil depth. The decreasing topsoil depth significantly reduced the maize leaf net photosynthetic rate, activities of key nitrogen metabolism enzymes, and photosynthesis. Therefore, eliminating intraspecific competition did not reduce the yield loss caused by a reduction in topsoil because leaf nitrogen metabolism and photosynthetic processes were severely limited by the decrease in topsoil depth. Full article

Phalaenopsis is a widely cultivated ornamental plant of considerable economic value worldwide. However, traditional growing medium, sphagnum moss, is limited and non-renewable. It also decomposes slowly and is prone to environmental issues. Therefore, there is an urgent need to identify more environmentally friendly and efficient alternatives. Biochar, a sustainable material with excellent physical and chemical properties, has been recognized as an effective promoter of plant growth. In this study, we investigated the influence of biochar derived from three raw materials (corn straw, bamboo, and walnut) mixed1 with coconut shell at ratios of 1:2, 1:10, and 4:1, on the growth of Phalaenopsis ‘Big Chili’. Over a 150-day controlled experiment, we evaluated multiple growth parameters, including plant height, crown width, total root length, total projected area, total surface area, and root volume. Compared to the traditional growing medium, the optimal biochar-coconut shell mixture (maize straw biochar: coconut shell = 1:2) increased plant height and crown width by 7.55% and 6.68%, respectively. Root metrics improved substantially, with total root length increasing by 10.96%, total projected area by 22.82%, total surface area by 22.14%, and root volume by 38.49%. Root biomass in the optimal treatment group increased by 42.47%, while aboveground and belowground dry weights increased by 6.16% and 77.11%, respectively. These improvements were closely associated with favorable substrate characteristics, including low bulk density, high total and water-holding porosity, moderate aeration, and adequate nutrient availability. These findings demonstrate that substrate characteristics critically influence plant performance and that biochar–coconut shell mixtures, particularly at a 1:2 ratio, represent a viable and sustainable alternative to sphagnum moss for commercial cultivation of Phalaenopsis. Full article

The planting area of the maize–soybean strip intercropping system has been increasing annually in the Hexi Corridor oasis irrigation area of China. However, long-term irrational water resource utilization and the excessive mono-application of fertilizers have led to significantly low water and nitrogen use efficiency in this cropping system. To explore the sustainable production model of high yield and high water–nitrogen productivity in maize–soybean strip intercropping, we established three irrigation levels (low: 60%, medium: 80%, and sufficient: 100% of reference crop evapotranspiration) and three nitrogen application levels (low: maize 230 kg ha⁻¹, soybean 29 kg ha⁻¹; medium: maize 340 kg ha⁻¹, soybean 57 kg ha⁻¹; and high: maize 450 kg ha⁻¹, soybean 85 kg ha⁻¹) for maize and soybean, respectively. Three irrigation levels without nitrogen application served as controls. The effects of different water–nitrogen combinations on multiple indicators of the maize–soybean strip intercropping system, including yield, water–nitrogen productivity, and quality, were analyzed. The results showed that the irrigation amount and nitrogen application rate significantly affected the kernel quality of maize. Specifically, the medium nitrogen and sufficient water (N2W3) combination achieved optimal performance in crude fat, starch, and bulk density. However, excessive irrigation and nitrogen application led to a reduction in the content of lysine and crude protein in maize, as well as crude fat and crude starch in soybean. Appropriate irrigation and nitrogen application significantly increased the yield in the maize–soybean strip intercropping system, in which the N2W3 treatment had the highest yield, with maize and soybean yields reaching 14007.02 and 2025.39 kg ha⁻¹, respectively, which increased by 2.52% to 138.85% and 5.37% to 191.44% compared with the other treatments. Taking into account the growing environment of the oasis agricultural area in the Hexi Corridor and the effects of different water and nitrogen supplies on the yield, water–nitrogen productivity, and kernel quality of maize and soybeans in the strip intercropping system, the highest target yield can be achieved when the irrigation quotas for maize and soybeans are set at 100% ET0 (reference crop evapotranspiration), with nitrogen application rates of 354.78~422.51 kg ha⁻¹ and 60.27~71.81 kg ha⁻¹, respectively. This provides guidance for enhancing yield and quality in maize–soybean strip intercropping in the oasis agricultural area of the Hexi Corridor, achieving the dual objectives of high yield and superior quality. Full article

Soil health is vital for food security and ecosystem services supporting climate change mitigation. Cover crops (CCs) improve soil quality and crop yields in intensive agriculture. This study assessed the impact of Sinapis alba L. as a CC on ten physical, chemical, and biological soil indicators before maize planting. Three management systems were compared: (i) CC with conventional tillage (CT), (ii) CC under no tillage (NT), and (iii) tilled fallow without CC (TF). Measurements were taken at 60 and 90 days after sowing (DAS) at 0–6 and 0–20 cm depths. The Soil Quality Index (SQI) was higher at the surface under NT (0.69 at 60 DAS; 0.65 at 90 DAS). At 0–20 cm, SQI values increased at 90 DAS but did not differ among treatments. TF also showed improvements (up to +18% at 0–20 cm). Dissolved organic matter increased significantly (1.7–2.5 times), especially under NT and CT. NT enhanced structural stability (+70%) and reduced bulk density (−47%). All glomalin fractions decreased at 90 DAS; however, NT retained higher concentrations of recalcitrant glomalin in the 0–6 cm layer compared to the other treatments. These findings highlight S. alba under no tillage as a promising strategy to improve soil quality, though long-term studies are needed. Full article

Northeast China is a key grain production region yet achieving coordinated improvements in maize yield and quality across diverse environments remains challenging. This study conducted a meta-analysis to evaluate maize yield and quality responses to chemical fertilizer inputs under varying natural (climate, soil) and anthropogenic (fertilization, planting) conditions. The results indicated that fertilizer application increased yield by 20.0%, and protein, fat, and starch contents by 12.6, 1.4, and 1.2%, respectively, compared to no fertilization. Yield response was highest under precipitation <450 mm and temperatures >7 °C, while protein and fat gains were favored by >600 mm precipitation and 5–7 °C temperatures. Soils with pH <6.5 and saline–alkaline properties supported greater yield gains, while brown and black soils promoted protein and fat accumulation, respectively. Moderate nutrient inputs (N 180–240, P₂O₅ 75–120, K₂O 90–135 kg ha⁻¹) outperformed lower or higher levels in improving both traits, with planting density also affecting response magnitude. Yield gains were primarily driven by soil fertility, whereas quality improvements were influenced by climate and management. Moderate fertilization facilitated the simultaneous enhancement of yield and quality. Tailored nutrient strategies based on soil and climate conditions can support regional maize productivity and contribute to food security. Full article

Plant height (PH) and ear height (EH) are closely related to dense planting characteristics and lodging resistance of maize (Zea mays L.). Increasing the planting density will lead to changes in the structural characteristics of maize plants, such as reduced stem length and stem strength, thereby influencing their yield and quality. Therefore, analyzing the genetic basis of PH and EH in maize can provide valuable information for cultivating ideal plant types with suitable PH and EH. This study aims to identify stable genomic regions and candidate genes associated with PH and EH in maize through Meta-QTL (MQTL) analysis. A total of 187 original QTLs were collected from 13 published articles on QTL localization related to maize PH and EH. A high-density consistency map with a total length of 6970.00 cM was constructed, and 152 original QTLs were successfully projected into the consistency map. The remaining 35 QTLs could not be projected onto the consistency map, which may be attributed to a lack of common markers between the original and consistency map or to the QTL exhibiting low phenotypic variance explained (PVE), resulting in large confidence intervals (CIs). Then, 29 MQTLs were identified on 10 chromosomes via meta-analysis. Among them, the three identified MQTLs, i.e., MQTL4-1, MQTL4-2, and MQTL6-1, were specifically controlled by maize EH. Further analysis achieved 188 candidate genes in all MQTL intervals, which were related to maize plant development and morphogenesis. Meanwhile, the gene ontology (GO) enrichment analysis revealed that these candidate genes were involved in 77 GO annotations. These findings thus will help us better understand the molecular genetic basis of maize PH and EH under various environments, and thereby achieve an increased yield with maize dense planting breeding. Full article

Spodoptera frugiperda, a major global agricultural pest, poses significant challenges to chemical control methods due to pesticide resistance and environmental concerns, underscoring the need for sustainable management strategies. Attractants based on host plant volatiles offer a promising eco-friendly approach, but their development for S. frugiperda is hindered by limited research on host recognition mechanisms. This study reveals that female S. frugiperda preferentially oviposit on maize at the seedling stage. Using electrophysiological techniques, we identified p-xylene and (+)-camphor from seedling-stage maize volatiles as key compounds eliciting strong responses in female S. frugiperda. Behavioral assays confirmed that these compounds (p-xylene at the concentration of 5%, 10%, and 20% and (+)-camphor at 1%, 5%, and 10%) significantly attract females, establishing them as the key odor cues for host selection. Moreover, these volatiles are more abundant in seedling-stage maize, suggesting that S. frugiperda assesses maize growth stages based on their concentrations. Importantly, larvae reared on seedling-stage maize exhibited higher survival rates than those on later-stage maize, indicating that oviposition site selection directly affects offspring fitness. These findings demonstrate that S. frugiperda uses p-xylene and (+)-camphor to evaluate maize development and select suitable oviposition sites, thereby enhancing larval survival. This study provides a foundation for developing targeted attractants for S. frugiperda and highlights the seedling stage as a critical period for implementing pest control measures, particularly in autumn maize production, given the higher pest population density during this phase. Full article

This study systematically investigates the mechanistic effects of multifactor interactions (including soil properties, climatic conditions, and cultivation practices) on the productivity parameters (grain yield, stover yield, dry biomass, harvest index) of maize cultivars of different maturity groups in the arid region of Xinjiang, China. Twelve representative maize-growing counties were selected as study sites, where we collected maize samples to measure HI, grain yield, stover yield, and soil physicochemical properties (e.g., organic matter content, total nitrogen, and available phosphorus). Additionally, climate data (effective accumulated temperature) and agronomic parameters (planting density) were integrated to comprehensively analyze the interactive effects of multiple environmental factors on HI using structural equation modeling (SEM). The results demonstrated significant varietal differences in HI across maturity periods. Specifically, early-maturing cultivars showed the highest average HI (0.58), significantly exceeding those of medium-maturing (0.55) and late-maturing varieties (0.54). Environmental analysis further revealed that soil phosphorus content (both available and total phosphorus), elevation, and organic matter content significantly positively affected HI, whereas soil bulk density and electrical conductivity exhibited negative impacts. Notably, HI exhibited a strong negative correlation with stover yield (R² = 0.49), but remained relatively stable across different dry matter (DM) and grain yield levels. Despite the strong positive correlation between DM and grain yield (R² = 0.81), the relative stability of HI suggests that yield improvement requires balanced optimization of both DM and partitioning efficiency. This study provides crucial theoretical foundations for optimizing high-yield maize cultivation systems, regulating fertilizer application rates and their ratios, and improving the configuration of planting density in arid regions. These findings offer practical guidance for sustainable agricultural development in similar environments. Full article

Salinity stress severely limits maize (Zea mays L.) productivity, necessitating sustainable mitigation strategies to ensure food security in affected regions. This study investigates the efficacy of compost (5 and 10 t/ha) and plant growth-promoting rhizobacteria (PGPR; Azospirillum brasilense) in enhancing maize productivity and soil health under salinity stress (EC_e 3.5 and 6.3 dS/m) across three varieties (Single Cross 131, 132, and 178) in field experiments conducted in 2023 and 2024. Combined compost-10 + PGPR treatment significantly increased grain yield by up to 197% and straw yield by nearly 300% in Single Cross 178 under high salinity, surpassing single treatments. Nitrogen content in grains and straw rose by 157%, while proline, peroxidase activity, and chlorophyll content improved, indicating robust stress tolerance. Soil properties, including pH, EC_e, sodium adsorption ratio, and exchangeable sodium percentage, were significantly ameliorated, with bulk density reduced and porosity increased. Soil organic matter and microbial populations (bacteria and fungi) were also enhanced. Single Cross 178 exhibited superior stress tolerance, highlighting varietal differences. These findings, supported by comparisons with the existing literature, underscore the synergistic role of compost and PGPR in improving nutrient uptake, antioxidant defenses, and soil structure. This study offers a sustainable strategy for maize cultivation in saline environments, with implications for global food security. Full article

This study aimed to investigate the effects of different planting density and row spacing configurations on maize corn yield, leaf photosynthetic parameters, and senescence characteristics; to reveal the purpose of the physiological mechanism of row density interaction regulatsving maize yield; and to clarify the optimal planting combinations for optimizing population structure, delaying leaf senescence, and improving light energy utilization efficiency. In doing so, this study provides a theoretical basis and technical guidance for increasing corn yield, the sustainable development of the maize industry, and improved yield production in Shanxi Province. An experiment was conducted with a two-factor randomized block design, with three planting densities of 60,000 plants/hm² (D1), 67,500 plants/hm² (D2), and 75,000 plants/hm² (D3) in the main area and four-row spacings of 40 + 40 cm, 40 + 80 cm, 50 + 50 cm, and 80 + 80 cm in the secondary area. The maize kernel yield, leaf photosynthetic parameters, malondialdehyde content, and anti-aging key enzyme activities were measured in 2023 and 2024. The results show that with the increase in planting density, the net photosynthetic rate of maize leaves gradually decreased, and the transpiration rate gradually increased. At the same time, too high or too low density will accelerate the aging of maize leaves, which is manifested by the increase in MDA (malondialdehyde) content and the decrease in SOD (superoxide dismutase) and CAT (catalase) activities. The best row spacing configuration performance is 40 + 80 cm, which is conducive to the ventilation and light transmission of maize plants, improves the efficiency of light energy utilization, slows down the aging of plant leaves, and thus promotes maize growth, development, and yield enhancement. The interaction effect between two intercropping maize factors significantly affects corn yield, with a medium density of 67,500, where 6000 is the most effective. Thus, 67,500 plants/hm² combined with a row spacing of 40 + 80 cm significantly increases corn yield. This combination obtained the highest net photosynthesis, SOD, and CAT of 24.33 µmol·m⁻²·s⁻¹, 32.54 U·mg⁻¹ and 1038.99 U·g⁻¹, and the lowest transpiration rate and MDA content of 3.47 mmo·m⁻²·s⁻¹ and 108.95 µmo·L⁻¹, resulting in the highest maize yield of 13,916.46 kg/hm². In summary, a density of 67,500 plants/hm² and 40 + 80 cm row spacing is the best combination, improving light energy utilization efficiency, delaying the leaf senescence process, and increasing the yield, which can provide a theoretical reference for the planting pattern of maize in Shanxi Province. Full article