Search Results (177)

Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R² values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R² values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R² values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article

Breeding and cultivating low-N-efficient maize varieties to obtain high yields with less N fertilizer is important for addressing food demands and environmental pollution. However, few studies have investigated the physiological characteristics of low-N-efficient maize varieties. Therefore, we performed an experiment over four years to test two maize varieties (low-N-efficient variety: JNK728, and high-N-efficient variety: XY335) and five N application rates (N120: 120 kg·ha⁻¹, N180: 180 kg·ha⁻¹, N240: 240 kg·ha⁻¹, N300: 300 kg·ha⁻¹, and N360: 360 kg·ha⁻¹). The optimal N application rates for JNK728 and XY335 were N180 and N300, which obtained the highest yields (11,754 and 12,752 kg·ha⁻¹, respectively), N uptake efficiencies (1.32 and 0.93 kg·kg⁻¹), and N harvest index (67.94% and 61.98%), compared with other N application rates. The key period for plant N accumulation was the R1–R6 stage, which contributed 35.2–49.7% and 40.8–53.8% to plant N accumulation at the maturation stage in JNK728 and XY335, respectively. In addition, N accumulation in the grain accounted for more than half (51.8–63.2%) of the total N accumulation in plants, and the leaf N transport amount after the post-silking stage was the primary source of grain N accumulation in both JNK728 and XY335. We also explored the key enzymes and genes related to the N transport amount and efficiency in leaves in the two maize varieties, and found that GOGAT was the key enzyme and GOGAT2 was the key gene for JNK728, whereas the AS enzyme and AS1 and AS3 genes were most important for XY335. Therefore, we suggest that molecular breeding programs should focus on the GOGAT2 gene in low-N-efficient maize varieties, and cultivation techniques should aim to improve the GOGAT enzyme activity after the post-silking period to achieve high yields and N utilization efficiencies with less N fertilizer. Full article

Increasing the nitrogen (N) use efficiency (NUE) of modern high-yield maize hybrids is essential for food security and reducing environmental risks. However, the relationship between dry matter (DM), N accumulation, and reallocation among different high-yield maize hybrids and NUE, particularly under various N fertilization levels, is not well understood. The field experiment was conducted in Jilin Province, Northeast China. In this study, two maize hybrids, Zhengdan958 (ZD958) and Tie 391 (T391), were grown under four N fertilizer levels: 0 (NN), 120 (LN), 240 (MN), and 360 (HN) kg ha⁻¹. We examined the effects of N input on grain yield, NUE, DM, and N accumulation, partitioning, and reallocation of these two high-yielding maize hybrids during the 2023–2024 growing season. The results showed that N input significantly increased grain yield but reduced NUE. There was no significant difference in yield and NUE between the two maize hybrids at the HN level. However, under LN conditions, the grain yield and NUE of ZD958 were higher by 16.2% and 15.6%, respectively, compared to T391. Meanwhile, ZD958 exhibited greater per-silking and post-silking DM (5.0% and 7.9%) and N accumulation (11.6% and 32.7%), as well as a higher amount of reallocated DM (45.6%) and N (17.5%) compared to T391. Moreover, 15.5–38.1% of grain N for ZD958 and 17.2–46.7% for T391 still needed to be reallocated from vegetative organs, with a larger fraction coming from the stem rather than the leaves. The middle leaves and lower stems of the canopy tended to reallocate more N to the grain, and lower-layer stem N reallocation was significantly related to grain yield. In conclusion, higher accumulation of DM and N, along with greater N reallocation—especially from the lower-layer stem—could be regarded as important traits in maize breeding to improve the NUE of high-yield maize hybrids under insufficient N supply. Full article

This research investigates the influence of water–nitrogen coupling on soil water content, nitrogen dynamics, and root distribution in farmland, along with the interactions among soil water, nitrogen transport, root distribution, and crop yield. A field experiment was conducted under moderate drought stress (50–60% of field capacity) and three nitrogen application rates (100, 200, and 300 kg·ha⁻¹, split-applied at 50% during sowing and 50% at the jointing stage, labeled as N₁, N₂, and N₃) at the two critical growth stages (jointing stage P₁ and tasseling-silking stage P₂) of maize (Denghai 605). The results demonstrated that maize root morphological parameters exhibited the trend N₂ > N₁ > N₃ under different nitrogen treatments. Compared to N₂, low nitrogen (N₁) decreased root morphological parameters by 35.01–49.60% on average, whereas high nitrogen (N₃) led to a reduction of 49.93–61.37%. The N₂ treatment consistently maintained greater water uptake, with the highest yield of 13,336 kg·ha⁻¹ observed under the CKN₂ treatment, representing increases of 16.1% and 9.2% compared to the P₁N₂ and P₂N₂ treatments, respectively. Drought stress at the jointing stage (P₁) inhibited root development more severely than at the tasseling-silking stage (P₂), demonstrating a bidirectional adaptation strategy characterized by deeper vertical penetration under water stress and increased horizontal expansion under nitrogen imbalance. Correlation analysis revealed a positive correlation between soil nutrient content and maize yield indicators. At the same time, root characteristic values were significantly negatively correlated with yield (p < 0.05). Appropriate water–nitrogen management effectively stimulated root growth, mitigated nitrogen leaching risks, and improved yield. These findings offer a theoretical foundation for optimizing water and nitrogen management in maize production within the Yellow River Basin. Full article

Nitrogen (N) is critical for maize (Zea mays L.) growth and yield, necessitating precise estimation of canopy nitrogen concentration (CNC) to optimize fertilization strategies. Remote sensing technologies, such as proximal hyperspectral sensors and unmanned aerial vehicle (UAV)-based multispectral imaging, offer promising solutions for non-destructive CNC monitoring. This study evaluates the effectiveness of proximal hyperspectral sensor and UAV-based multispectral data integration in estimating CNC for spring maize during key growth stages (from the 11th leaf stage, V11, to the Silking stage, R1). Field experiments were conducted to collect multispectral data (20 vegetation indices [MVI] and 24 texture indices [MTI]), hyperspectral data (24 vegetation indices [HVI] and 20 characteristic indices [HCI]), alongside laboratory analysis of 120 CNC samples. The Boruta algorithm identified important features from integrated datasets, followed by correlation analysis between these features and CNC and Random Forest (RF)-based modeling, with SHAP (SHapley Additive exPlanations) values interpreting feature contributions. Results demonstrated the UAV-based multispectral model achieved high accuracy and Computational Efficiency (CE) (R² = 0.879, RMSE = 0.212, CE = 2.075), outperforming the hyperspectral HVI-HCI model (R² = 0.832, RMSE = 0.250, CE =2.080). Integrating multispectral and hyperspectral features yields a high-precision model for CNC model estimation (R² = 0.903, RMSE = 0.190), outperforming standalone multispectral and hyperspectral models by 2.73% and 8.53%, respectively. However, the CE of the integrated model decreased by 1.93% and 1.68%, respectively. Key features included multispectral red-edge indices (NREI, NDRE, CI) and texture parameters (R1m), alongside hyperspectral indices (SR, PRI) and spectral parameters (SDy, Rg) exhibited varying directional impacts on CNC estimation using RF. Together, these findings highlight that the Boruta–RF–SHAP strategy demonstrates the synergistic value of integrating multi-source data from UAV-based multispectral and proximal hyperspectral sensing data for enhancing precise nitrogen management in maize cultivation. Full article

In strip intercropping, increasing bandwidth enhances light energy utilization and facilitates mechanized production, yet it constrains the realization of maize yield advantages. The impact of bandwidth on the ear differentiation and development and yield formation requires further investigation. In this study, different bandwidths (T1, 1.6 m, T2, 2.0 m, T3, 2.4 m, and T4, 2.8 m) were arranged, and monoculture maize with varying row spacings (K1, 0.8 m, K2, 1.0 m, K3, 1.2 m, and K4, 1.4 m) was used as the control. The results show that increasing bandwidth inhibited the ear differentiation. The proportion of dry matter partitioning to leaves increased and to ears decreased, resulting in shorter ear length and higher floret and grain abortion rates. Maize yield losses amounted to 26.9% and 31.6% in T4 compared to K4 and T1, respectively. Moreover, the bandwidth did not affect the fertilized florets due to the smaller anthesis–silking interval created by the simultaneous effect. We concluded that the appropriate bandwidth, 1.6 m and 2.0 m, can stabilize the dry matter partitioning to the ear; stabilize ear length, floret, and grain abortion rate; and stabilize the maize yield. Full article

Germplasm improvement is essential for maize breeding. Currently, intra-heterotic-group crossing is the major method for germplasm improvement, while inter-heterotic-group crossing is also used in breeding but not in a systematic way. In this study, five inbred lines from four heterotic groups were used to develop a connected segregating population through inter-heterotic-group line crossing (CSPIC), which comprised 5 subpopulations with 535 doubled haploid (DH) lines and 15 related test-cross populations including 1568 hybrids. Significant genetic variation was observed in most subpopulations, with several DH populations exhibiting superior phenotypes regarding traits such as plant height (PH), ear height (EH), days to anthesis (DTA), and days to silking (DTS). Notably, 10.8% of hybrids in the population POP5/C229 surpassed the high-yielding hybrid ND678 (CK). To reduce field planting costs and quickly screen for the best inter-heterotic-group DH lines and test-cross hybrids, we assessed the accuracy of genomic selection (GS) for within- and between-population predictions in the DH populations and the test-cross populations. Within the DH or the hybrid population, the prediction accuracy varied across populations and traits, with an average hybrid yield prediction accuracy of 0.41, reaching 0.54 in POP5/Z58. In the cross DH population predictions, the prediction accuracy of the half-sib population exceeded that of the non-sib cross population prediction, with the highest accuracy observed when the non-shared parents were from the same heterotic group, and the average phenotypic prediction accuracies of POP3 predicting POP2 and POP2 predicting POP3 were 0.54 and 0.45, respectively. In the cross hybrid population predictions, the accuracy was highest when both the training and the test sets came from the same DH populations, with an average accuracy of 0.43. The proportion of shared polymorphisms with respect to SNPs between the training and the test sets (PSP) exhibited a significant and strong correlation with the prediction accuracy of cross population prediction. This study demonstrates the feasibility of creating new heterotic groups through inter-heterotic-group crossing in germplasm improvement, and some cross population prediction patterns exhibited excellent prediction accuracy. Full article

Enhancing chlorophyll retention in maize leaves and prolonging the grain-filling duration constitute critical strategies for yield improvement in agricultural production systems. This study investigated the mechanistic relationship between yield enhancement pathways and the leaf senescence process induced by high-input straw pellets amendment. We analyzed the impact mechanisms of green leaf area dynamics and dry matter redistribution on yield during late reproductive stages, establishing theoretical foundations for yield optimization through intensive straw pellets incorporation. The study used the maize variety Jingnongke 728 as the experimental material. Based on previous research, four treatments were set up, including no straw returning (CK), chopped straw (15 t/ha) returning to the field (FS1), a large amount of chopped straw (75 t/ha) returning to the field (FS5), and a large amount of pelletized straw (75 t/ha) returning to the field (KL5), with four replicates. A two-year experimental design systematically assessed green leaf area index (GLAI), dry matter accumulation, distribution, translocation, yield components, and grain yield to explore the differences among various treatments under different straw returning amounts and returning forms. The study detected no significant differences between FS1 and CK. Although KL5 and FS5 delayed leaf senescence, FS5 significantly depressed green leaf area index (GLAI) at the R1 stage (silking), which results in it not having more effective photosynthetic area during late phenological phases. In dry matter dynamics, KL5 exhibited 5.52–25.71% greater pre-anthesis accumulation, 2.73–60.74% higher post-anthesis accumulation, and 9.48–25.76% elevated ear dry matter allocation relative to other treatments. KL5’s post-anthesis assimilates contributed 2.43–17.02% more to grain development, concurrently increasing ear-to-total biomass ratio. Yield analysis ranked KL5 as the superior treatment with 0.68–25.15% yield advantage, driven by significantly enhanced kernel number per ear and 100-kernel mass, whereas FS5 displayed the lowest kernel count among all treatments. Returning 75 t/ha of straw pellets to the black soil area in Northeast China can significantly delay the senescence of maize leaves and increase the accumulation of dry matter after anthesis by maintaining the effective photosynthetic area of leaves in the later stage of growth, thereby achieving the goal of increasing yield. The research can offer a practical and novel approach for straw return in the black soil region of Northeast China and provide a new technological pathway for enhancing crop productivity. Full article

Maize (Zea mays L.) is an important crop grown for food, feed, and energy. In general, maize yield is decreased due to drought stress during the reproductive stages, and, hence, it is critical to improve the grain yield under drought. A field experiment was conducted with a split-plot design. The main factor was the irrigation regime viz. well-irrigated conditions and withholding irrigation from tasseling to grain filling for 21 days. The subplots include six treatments, namely, (i) the control (water spray), (ii) zinc oxide @ 100 ppm, (iii) manganese oxide @ 20 ppm, (iv) nZnO @ 100 ppm + nMnO @ 20 ppm, (v) Tamil Nadu Agricultural University (TNAU) Nano Revive @ 1.0%, and (vi) zinc sulfate 0.25% + manganese sulfate 0.25%. During drought stress, the anthesis–silking interval (ASI), chlorophyll a and b content, proline, starch, and carbohydrate fractions were recorded. At harvest, the grain-filling rate and duration, per cent green leaf area, and yield traits were recorded. Drought stress increased the proline (38.1%) and anthesis–silking interval (0.45 d) over the irrigated condition. However, the foliar application of ZnO (100 ppm) and nMnO (20 ppm) lowered the ASI and increased the green leaf area, leaf chlorophyll index, and proline content over water spray. The seed-filling rate (17%), seed-filling duration (11%), and seed yield (19%) decreased under drought. Nevertheless, the seed-filling rate (90%), seed-filling duration (13%), and seed yield (52%) were increased by the foliar spraying of nZnO (100 ppm) and nMnO (20 ppm) over water spray. These findings suggest that nZnO and nMnO significantly improve the grain yield of maize under drought stress conditions. Full article

The improper application of phosphorus (P) fertilizers not only leads to resource wastage and environmental concerns but also disrupts the normal growth and yield formation of maize. This study aims to explore the effects of varying P application rates on the growth, yield, photothermal response characteristics, P accumulation dynamics, and P recovery efficiency (PRE) in summer maize, which provides a theoretical foundation for the efficient and scientific application of P fertilizers. Field experiments were conducted over two growing seasons (2021−2022) in Wen County, Henan Province, with P application rates set at 0, 30, 60, 90, and 120 kg·P₂O₅·ha⁻¹. At maturity, maize yield and its components were quantified. During key growth stages—jointing, tasseling, silking, and grain filling—plant height, leaf area, Soil and Plant Analyzer Development (SPAD) value, the fraction of photosynthetically active radiation (FPAR), canopy temperature, acid phosphatase activity (ACP), and P accumulation were measured. The results indicated that maize grain yield initially increased with P application, peaking at an average increase of 7.92–15.88%, before decreasing. The optimal P application rates were determined to be 113 kg·P₂O₅·ha⁻¹ and 68 kg·P₂O₅·ha⁻¹, respectively. P application significantly lowered canopy temperature and leaf ACP activity while significantly increasing the SPAD value and FPAR at 90 kg·P₂O₅·ha⁻¹. Logistic regression analysis of P accumulation revealed that increasing P rates enhanced the maximum (V_max) and mean (V_mean) accumulation rates, as well as the total P accumulation. Moderate P application also improved P absorption in various plant tissues and promoted the transfer of P to the grains. However, PRE, partial factor productivity from P fertilizer (PPFP), and P agronomic efficiency (PAE) declined at higher P rates. In conclusion, P fertilization enhanced maize yield, promoted growth, improved P utilization, and optimized photothermal response characteristics across different growth stages. Based on these findings, the recommended P application rate for summer maize is between 70 and 110 kg·P₂O₅·ha⁻¹. Full article

Drought is the main stress on maize, and, in order to improve drought tolerance, a breeding program for reduced anthesis-silking interval (ASI) was carried out in Algiers. The objective of this study was to investigate if the selection for reduced ASI made in Algiers had a positive effect on drought tolerance in northern Spain. Two populations selected for reduced ASI in Algiers were evaluated in Algiers and Pontevedra (northwestern Spain) under well-watered and drought conditions. The dry trial was not irrigated, while the well-watered trial was irrigated three times. Data were taken on agronomic and photosynthetic traits in the selection of reduced ASI and anthesis and increased yield for BTM and LOM. In the combined analyses of variance in locations, differences were significant among environments and among genotypes for most agronomic traits. Rank correlations between Algiers and Pontevedra were high and significant for flowering and correlations were higher when measured under the same water regime. In the Spanish environments, differences between the drought and well-watered selection and differences among genotypes within water regimens were significant for most agronomic traits. The agronomic performance of the selection cycles under drought and well-watered conditions indicated that selection for reducing ASI in Algiers was partially effective in Pontevedra. Photosynthetic traits did not respond to selection for ASI; nevertheless, stomatal conductance had positive effects and water use efficiency had a negative effect on plant height and yield. Therefore, base breeding populations after prebreeding in arid environments could be used for breeding programs in humid environments, and some physiological traits had limited effects on plant growth and yield. Full article

Fresh sweet and waxy maize (Zea mays) are valuable specialty crops in southern China. Hyperspectral remote sensing offers a powerful tool for detecting maize foliar nutrients non-destructively. This study aims to investigate the capability of leaf spectroscopy (SVC HR-1024i spectrometer, wavelength range: 400–2500 nm) to retrieve maize foliar nutrients. Specifically, we (1) explored the effects of nitrogen application rates (0, 150, 225, 300, and 450 kg·N·ha⁻¹), maize cultivars (GLT-27 and TGN-932), and growth stages (third leaf (vegetation V3), stem elongation stage (vegetation V6), silking stage (reproductive R2), and milk stage (reproductive R3)) on foliar nutrients (nitrogen, phosphorus, and carbon) and leaf spectra; (2) evaluated the transferability of the regression and physical models in retrieving foliar nutrients across maize cultivars. We found that the PLSR (partial least squares regression), SVR (support vector machine regression), and RFR (random forest regression) regression model accuracies were fair within a specific cultivar, with the highest R² of 0.60 and the lowest NRMSE (normalized RMSE = RMSE/(Max − Min)) of 17% for nitrogen, R² of 0.19 and NRMSE of 21% for phosphorous, and R² of 0.45 and NRMSE of 19% for carbon. However, when these cultivar-specific models were used to predict foliar nitrogen across cultivars, lower R² and higher NRMSE values were observed. For the physical model, which does not rely on the dataset, the R² and NRMSE for foliar chlorophyll-a and -b (C_ab), carotenoid (C_xc), and equivalent water thickness (EWT) were 0.76 and 15%, 0.67 and 34%, and 0.47 and 21%, respectively. However, the prediction accuracy for foliar nitrogen, expressed as foliar protein in PROSPECT-PRO, was lower, with an R² of 0.22 and NRMSE of 27%, which was comparable to that of the regression models. The primary reasons for this limited transferability were attributed to (1) the insufficient number of samples and (2) the lack of strong absorption features for foliar nutrients within the 400–2500 nm wavelength range and the confounding effects of other foliar biochemicals with strong absorption features. Future efforts are needed to investigate the physical mechanisms underlying hyperspectral remote sensing of foliar nutrients and incorporate transfer learning techniques into foliar nutrient models. Full article

Assessing genetic gains from historical data provides insights to improve breeding programs. This study evaluated the Mozambique National Maize Program’s (MNMP’s) genetic gains using data from advanced germplasm trials conducted at 21 locations between 2014 and 2020. Genetic gains were calculated by regressing the genotypic best linear unbiased estimates of grain yield and complementary agronomic traits against the initial year of genotype evaluation (n = 592). The annual genetic gain was expressed as a percentage of the trait mean. While grain yield, the primary breeding focus, showed no significant improvement, significant gains were observed for the plant height (0.67%), ear height (1.74%), ears per plant (1.31%), ear position coefficient (1.22%), and husk cover (4.7%). Negative genetic gains were detected for the days to anthesis (−0.5%), the anthesis–silking interval or ASI (−9.31%), and stalk lodging (−5.01%). These results indicate that while MNMP did not achieve the desired positive genetic gain for grain yield, progress was made for traits related to plant resilience, particularly the ASI and stalk lodging. MNMP should seek to incorporate new breeding technologies and human resources to enhance genetic gains for grain yield and other key traits in the maize breeding program, while developing and deploying high-yielding, climate-resilient maize varieties to address emerging food security challenges in Mozambique. Full article

Background: As a major food crop, maize is highly susceptible to pathogenic bacteria, which greatly reduces its yield and quality. Metabolomics reveals physiological and biochemical changes in organisms and aids in analyzing metabolic changes caused by various factors. Methods: This study utilized metabolomics to examine maize’s metabolic changes after NCLB infestation, aiming to uncover related pathways and potential biomarkers. The metabolite measurements were performed during the maize silking stage. Results: PCA showed an obvious dispersion between the treated and untreated groups. OPLS-DA identified 1274 differential metabolites, with 242 being downregulated (mainly phenolics and esters) and 1032 upregulated (primarily organic acids, amino acids, sugars, and derivatives). KEGG annotation revealed 50 affected metabolic pathways, and the biosynthesis of secondary metab-olites and amino acids was significantly enriched. Conclusions: We hypothesized that metabolic pathways related to sugar metabolism, proline metabolism, and jasmonic acid synthesis are associated with NCLB susceptibility. These findings provide critical insights into the metabolic responses of maize to biotic stress, offering a theoretical basis for future research on plant resistance mechanisms. Full article

Northeast China (NEC) is a major spring maize (Zea mays L.) growing belt, and the outputs substantially influence national grain production. However, the maize grain yield per unit area has little changes in recent years, partially due to the lack of elite germplasm resources and innovation. Therefore, this study aimed to determine the performance of diverse populations in NEC to propose appropriate strategies for the utilization of elite germplasm to broaden the genetic base of Chinese germplasm. Fifteen diverse maize populations from the International Maize and Wheat Improvement Center (CIMMYT) and the U.S. were crossed to two local tester lines, representing Chinese heterotic groups Reid and Lancaster, for evaluating the combining ability and heterosis in three locations (Gongzhuling, Jilin Province, and Harbin and Suihua, Heilongjiang Province) in NEC over two years. The U.S. (BS13(S)C7 and BS31) and Chinese (Ji Syn A) populations exhibited more favorable alleles for high yield potential in all locations tested. Furthermore, the PH6WC × BS31 and PH6WC × Ji Syn A crosses had higher grain yields, and an appropriate number of days to silking, ear height, and resistance to lodging at Gongzhuling and Harbin in NEC. The best strategies for utilizing these diverse germplasms may be to develop new inbred lines from the existing elite populations or improve the grain yield and resistance to lodging of the elite line PH4CV for broadening the genetic base of the Chinese group Lancaster in NEC. Full article