Search Results (598)

Tillage is a major factor influencing soil biological communities, particularly earthworms, which play a key role in soil structure and nutrient cycling. To address soil degradation, less-intensive tillage practices are increasingly being adopted globally and have shown positive effects on earthworm populations when applied consistently over extended periods. However, understanding of the earthworm population dynamics in the period following the implementation of changes in tillage practices remains limited. This three-year field study (2021–2023) investigates earthworm populations during the early transition phase (4–6 years) following the conversion from conventional ploughing to conservation (<8 cm depth, with residue retention) and no-tillage systems in a temperate arable system in central Slovenia. Earthworms were sampled annually in early October from three adjacent fields, each following the same three-year crop rotation (maize—winter cereal + cover crop—soybeans), using a combination of hand-sorting and allyl isothiocyanate (AITC) extraction. Results showed that reduced tillage practices significantly increased both earthworm biomass and abundance compared to conventional ploughing. However, a significant interaction between tillage and year was observed, with a sharp decline in earthworm abundance and mass in 2022, likely driven by a combination of 2022 summer tillage prior to cover crop sowing and extreme drought conditions. Juvenile earthworms were especially affected, with their proportion decreasing from 62% to 34% in ploughed plots and from 63% to 26% in conservation tillage plots. Despite interannual fluctuations, no-till showed the lowest variability in earthworm population. Long-term monitoring is essential to disentangle management and environmental effects and to inform resilient soil management strategies. Full article

In drought-prone regions without irrigation systems, effective agrotechnologies such as subsoiling are crucial for enhancing soil infiltration and water retention. However, the effects of subsoiling can vary depending on crop type and environmental conditions. Despite previous research, there is limited understanding of the contrasting responses of C3 (sunflower) and C4 (maize) crops to subsoiling under drought stress. This study addresses this knowledge gap by assessing the effectiveness of subsoiling as a drought mitigation practice on Haplic Chernozem in Northern Bulgaria, integrating ground-based and remote sensing data. Soil physical parameters, leaf area index (LAI), canopy temperature, crop water stress index (CWSI), soil moisture, and yield were evaluated under both conventional tillage and subsoiling for the two crops. A variety of optical and radar descriptive remote sensing products derived from Sentinel-1 and Sentinel-2 satellite data were calculated for different crop types. Consequently, the use of machine learning, utilizing all the processed remote sensing products, enabled the reasonable prediction of LAI, achieving a coefficient of determination (R²) after a cross-validation greater than 0.42 and demonstrating good agreement with in situ observations. Results revealed differing responses: subsoiling had a positive effect on sunflower, improving LAI, water status, and slightly increasing yield, while it had no positive effect on maize. These findings highlight the importance of crop-specific responses in evaluating subsoiling practices and demonstrate the added value of integrating unmanned aerial systems (UAS) and satellite-based remote sensing data into agricultural drought monitoring. Full article

Nitrogen use efficiency remains the primary bottleneck for sustainable maize production. This study elucidates the functional mechanisms of the amino acid transporter ZmAAP1 in nitrogen absorption and stress resilience. Through systematic evolutionary analysis of 55 maize inbred lines, we discovered that the ZmAAP1 gene family exhibits distinct chromosomal localization (Chr7 and Chr9) and functional domain diversification (e.g., group 10-specific motifs 11/12), indicating species-specific adaptive evolution. Integrative analysis of promoter cis-elements and multi-omics data confirmed the root-preferential expression of ZmAAP1 under drought stress, mediated via the ABA-DRE signaling pathway. To validate its biological role, we generated transgenic maize lines expressing Arabidopsis thaliana AtAAP1 via Agrobacterium-mediated transformation. Three generations of genetic stability screening confirmed the stable genomic integration and root-specific accumulation of the AtAAP1 protein (Southern blot/Western blot). Field trials demonstrated that low-N conditions enhanced the following transgenic traits: the chlorophyll content increased by 13.5%, and the aboveground biomass improved by 7.2%. Under high-N regimes, the gene-pyramided hybrid ZD958 (AAP1 + AAP1) achieved a 12.3% yield advantage over conventional varieties. Our findings reveal ZmAAP1’s dual role in root development and long-distance nitrogen transport, establishing it as a pivotal target for molecular breeding. This study provides actionable genetic resources for enhancing NUE in maize production systems. Full article

Climate change has intensified drought stress, threatening global food security by affecting sensitive crops like maize (Zea mays). This study evaluated the potential of Antarctic fungal endophytes (Penicillium chrysogenum and P. brevicompactum) to enhance maize drought tolerance under field conditions with different irrigation regimes. Drought stress reduced soil moisture to 59% of field capacity. UAV-based multispectral imagery monitored plant physiological status using vegetation indices (NDVI, NDRE, SIPI, GNDVI). Inoculated plants showed up to two-fold higher index values under drought, indicating improved stress resilience. Physiological analysis revealed increased photochemical efficiency (0.775), higher chlorophyll and carotenoid contents (45.54 mg/mL), and nearly 80% lower lipid peroxidation in inoculated plants. Lower proline accumulation suggested better water status and reduced osmotic stress. Secondary metabolites such as phenolics, flavonoids, and anthocyanins were elevated, particularly under well-watered conditions. Antioxidant enzyme activity shifted: SOD, CAT, and APX were suppressed, while POD activity increased, indicating reprogrammed oxidative stress responses. Yield components, including cob weight and length, improved significantly with inoculation under drought. These findings demonstrate the potential of Antarctic endophytes to enhance drought resilience in maize and underscore the value of integrating microbial biotechnology with UAV-based remote sensing for sustainable crop management under climate-induced water scarcity. Full article

Maize (Zea mays L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements. Full article

Efficient water management is critical for sustainable dryland agriculture, especially under increasing water scarcity and climate variability. Shanxi Province, a typical dryland region in northern China characterized by pronounced climatic variability and limited soil water availability, faces severe challenges due to uneven precipitation and restricted water resources. This study aimed to evaluate the spatiotemporal dynamics of soil water resources and their coupling with crop water demand under different hydrological year types. Using daily meteorological data from 27 stations (1963–2023), we identified dry, normal, and wet years through frequency analysis. Soil water resources were assessed under rainfed conditions, and water deficits of major crops—including millet, soybean, sorghum, winter wheat, maize, and potato—were quantified during key reproductive stages. Results showed a statistically significant declining trend in seasonal precipitation during both summer and winter cropping periods (p < 0.05), which corresponds with the observed intensification of crop water stress over recent decades. Notably, more than 86% of daily rainfall events were less than 5 mm, indicating low effective rainfall. Soil water availability closely followed precipitation distribution, with higher values in the south and west. Crop-specific analysis revealed that winter wheat and sorghum had the largest water deficits in dry years, necessitating timely supplemental irrigation. Even in wet years, water regulation strategies were required to improve water use efficiency and mitigate future drought risks. This study provides a practical framework for soil water–crop demand assessment and supports precision irrigation planning in dryland farming. The findings contribute to improving agricultural water use efficiency in semi-arid regions and offer valuable insights for adapting to climate-induced water challenges. Full article

Progressing climate change necessitates the development of drought-tolerant crops, and understanding the temporal dynamics of genotype x environment interactions (GxE) is crucial. This study aimed to test established phenotyping methods (chlorophyll a fluorescence (ChlF) and hyperspectral (HS) imaging) to investigate the variability in 165 inbred maize lines’ responses to progressive drought stress. The inbred maize lines were grown under controlled conditions and were challenged with water withholding. Fifteen ChlF and HS indices were measured at three consecutive time points (M1, M2, and M3). Mixed models were employed to estimate the GxT interaction effects via Best Linear Unbiased Predictors (BLUPs) for each variable. A Principal Component Analysis (PCA) performed on the GxT BLUPs from each time point revealed a highly dynamic interaction structure. While the primary axis of GxT variation (PC1) was consistently associated with HI, which is related to plant vigor, across all measurement times, its importance intensified under severe stress (M3). The secondary axis (PC2) shifted markedly over time: after initial variations at M1, it was dominated by GxT effects in specific ChlF parameters related to photosynthetic regulation under moderate stress (M2), before shifting again under severe stress (M3) to reflect the GxT effects on indices potentially related to pigment degradation and other stress indicators. Full article

Grain moisture influences grain number formation during the critical period as well as determining the final grain weight during the grain-filling period in maize (Zea mays L.). To clarify the relationships between grain number, grain weight, and grain moisture dynamics, a 2–year field experiment in a split-plot design was conducted with two irrigation treatments, well irrigation (WI) and no irrigation (NI), and with four husk removal treatments, including no husk removal as control (H₀) and removal of 1/4 (H_1/4), 2/4 (H_2/4), 3/4 (H_3/4), and 4/4 (H_4/4) of the husk layers, respectively. Husk removal reduced the maize grain number, grain dry weight, and yield, and the reductions were larger under no irrigation (33.4–33.5%) than under well irrigation conditions (27.7–33.2%). By contrast, irrigation increased grain water content by 11.1–13.4% and grain dry weight by 6.5–10.4%, regardless of husk removal. Meanwhile, the interactive effects between irrigation and husk removal were significant in grain water content but not in grain yield, reflecting the larger negative effects of husk removal on maize grain yield. In conclusion, husk plays a crucial role in grain number formation during the critical period and grain weight during the grain-filling period, especially in drought conditions, in relation to the trade-offs between yield enhancement and grain desiccation in maize production. Full article

The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (Zea mays L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods—random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)—were employed to systematically analyze the relevant traits of maize seedlings’ drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R² = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies. Full article

Drought severely limits crop yield every year, making it critical to clarify the genetic basis of drought tolerance for breeding of improved varieties. As drought tolerance is a complex quantitative trait, we analyzed three phenotypic groups: (1) agronomic traits under well-watered (WW) and water-deficit (WD) conditions, (2) stress tolerance indices of these traits, and (3) phenotypic plasticity, using a multi-parent doubled haploid (DH) population assessed in multi-environment trials. Genome-wide association studies (GWAS) identified 130, 171, and 71 quantitative trait loci (QTL) for the three groups of phenotypes, respectively. Only one QTL was shared among all trait groups, 25 between stress indices and agronomic traits, while the majority of QTL were specific to their group. Functional annotation of candidate genes revealed distinct pathways of the three phenotypic groups. Candidate genes under WD conditions were enriched for stress response and epigenetic regulation, while under WW conditions for protein synthesis and transport, RNA metabolism, and developmental regulation. Stress tolerance indices were enriched for transport of amino/organic acids, epigenetic regulation, and stress response, whereas plasticity showed enrichment for environmental adaptability. Transcriptome analysis of 26 potential candidate genes showed tissue-specific drought responses in leaves, ears, and tassels. Collectively, these results indicated both shared and independent genetic mechanisms underlying drought tolerance, providing novel insights into the complex phenotypes related to drought tolerance and guiding further strategies for molecular breeding in maize. Full article

Background: Casein kinase I-like (CKL) protein is a member of the serine/threonine kinase CKI family and plays a pivotal regulatory role in various eukaryotic cellular processes, including stress responses. Objectives: This study aims to systematically identify the CKL gene family in the tomato genome and investigate its responsiveness to abiotic stress. Methods: Members of SlCKL were identified through genome-wide bioinformatics analysis, and their physicochemical properties, chromosomal localization, gene structure, conserved domains, phylogenetic relationships, cis-acting elements, cross-species collinearity, and tissue expression profiles were comprehensively analyzed. The expression patterns of SlCKL genes under abiotic stress were validated using real-time quantitative PCR. Results: A total of 16 SlCKL genes were identified and classified into three subfamilies (I–III), which are unevenly distributed across nine chromosomes, predominantly clustered at the ends. The gene structure, motifs, and functional domains exhibit high conservation. Collinearity analysis revealed stronger synteny between tomato and Arabidopsis thaliana or pepper compared to rice, maize, or tobacco, suggesting a common ancestral origin. The tissue expression profile indicates that SlCKLs are preferentially transcribed in roots. Promoter analysis and qRT-PCR validation demonstrated differential responses of SlCKLs to various abiotic stresses, such as drought, salt, heat, cold, and ABA treatment. Conclusions: This study represents the first systematic identification of the tomato SlCKL gene family, elucidating its evolutionary relationships, structural characteristics, tissue-specific expression patterns, and differential responsiveness to abiotic stress, thereby providing a critical foundation for further investigation into the molecular mechanisms underlying CKL-mediated abiotic stress adaptation in tomatoes. Full article

Background/Objectives: A critical part of the maize life cycle takes place during the summer, and due to climate change, its growth and development are increasingly exposed to the irregular and unpredictable effects of drought stress. Developing and using new cultivars with increased drought tolerance for farmers is the easiest and cheapest solution. One of the concepts to screen for drought tolerance is to expose germplasm to various growth scenarios (environments), expecting that random drought will occur in some of them. Methods: In the present study, thirty-two maize hybrids belonging to four FAO maturity groups were tested for grain yield at six locations over two consecutive years. In parallel, data of the basic meteorological elements such as air temperature, relative humidity and precipitation were collected and used to compute two indices, scPDSI (Self-calibrating Palmer Drought Severity Index) and VPD (Vapor Pressure Deficit), that were assessed as indicators of drought (water deficit) severity during the vegetation period. Practical implementation of these indices was carried out indirectly by first analyzing yield data using a factor analytic model to detect latent environmental variables affecting yield and then correlating those latent variables with drought indices. Results: The first latent variable, which explained 47.97% of the total variability, was correlated with VPD (r = −0.58); the second latent variable explained 9.57% of the total variability and was correlated with scPDSI (r = −0.74). Furthermore, latent regression coefficients (i.e., genotypic sensitivities to latent environmental variables) were correlated with genotypic drought tolerance. Conclusions: This could be considered an indication that there were two different acting mechanisms in which drought affected yield. Full article

Estimating leaf water potential (Ψ_leaf) is essential for understanding plant physiological processes’ response to drought. The estimation of Ψ_leaf based on different regression analysis methods with hyperspectral vegetation indices (VIs) has been proven to be a simple and efficient technique. However, models constructed by existing methods and VIs still face challenges regarding the generalizability and limited ranges of field experiment datasets. In this study, leaf dehydration experiments of three maize cultivars were applied to provide a dataset covering a wide range of Ψ_leaf variations, which is often challenging to obtain in field trials. The analysis screened published VIs highly correlated with Ψ_leaf and constructed a model for Ψ_leaf estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. Models were constructed using PLSR and MLR for each cultivar and PLSR, MLR, and RF for the samples from all three cultivars. The performance of the models developed for each cultivar was compared with the performance of the cross-cultivar model. Simultaneously, the normalized ratio (ND) and double-difference (DDn) were applied to determine the VIs and models. Finally, the relationship between the optimal VIs and Ψ_leaf was analyzed using discontinuous linear segmental fitting. The results showed that leaf spectral reflectance variations in the 350~700 nm bands and 1450~2500 nm bands were significantly sensitive to Ψ_leaf. The RF method achieved the highest prediction accuracy when all three cultivars’ data were used, with a normalized root mean square error (NRMSE) of 9.02%. In contrast, there was little difference in the predictive effectiveness of the models constructed for each cultivar and all three cultivars. Moreover, the simple linear regression model built based on the DDn(2030,45) outperformed the RF method regarding prediction accuracy, with an NRMSE of 7.94%. Ψ_leaf at the breakpoint obtained by discontinuous linear segment fitting was about −1.20 MPa, consistent with the published range of the turgor loss point (Ψ_TLP). This study provides an effective methodology for Ψ_leaf monitoring with significant practical value, particularly in irrigation decision-making and drought prediction. Full article

Estimating the actual evapotranspiration (ET_{c act}) of cropland in arid areas, exploring the time trend, and analyzing periodic variation are the key to long-term assessment of water resource availability and regional drought. The Penman formula has a strong ability to characterize reference crop evapotranspiration (ET_o). However, the application of this formula may be limited in the absence of a complete set of climate data. While previous studies have investigated K_{c act} in China, few have employed localized K_c values to systematically analyze long-term periodic fluctuations in ET_{c act} under climate variability conditions. Therefore, this study aimed to evaluate the applicability of nine ET_o estimation models in the Loess Plateau of China, calculate actual crop coefficients (K_{c act}) for spring maize and winter wheat, and examine the temporal trend and periodicity of ET_{c act} for long-term (1961–2018) continuous cropping of spring maize and winter wheat in the study area. The Mann–Kendall test and continuous wavelet transform (CWT) were used to obtain the temporal trend and periodicity of ET_{c act}. The results were as follows: (1) Priestley–Taylor (Prs–Tylr), based on radiation, and the 1985 Hargreaves–Samani (Harg), based on temperature, can be used when meteorological data are limited. It should be noted that among the models evaluated in this study, except for FAO56-PM, only the Harg equation is compatible with K_c-ET_o due to established conversion factors. (2) The K_{c act} of spring maize at the seeding–jointing stage and the earning–filling stage was 12% and 10% lower than the value recommended by FAO, respectively. For K_{c act} of winter wheat, it was 65% higher, 31% lower, and 85% higher than the FAO experience values in the rejuvenation–jointing stage, heading–grouting stage, and grouting–harvest stage. (3) Winter wheat, through its ET_{c act} cycle synchronized with precipitation and excellent water balance, can effectively alleviate regional drought. It is recommended to be included in the promotion of drought resistance policies. Full article

Maize is one of the top five field crops worldwide and is indispensable as animal feed, serves as a raw material in many industries, and is a staple for human food. However, its production is under increasing pressure mainly due to abiotic stress. Drought and excessive precipitation, air temperature fluctuations, and reduced soil fertility due to inadequate soil pH reactions are among the biggest challenges that must be overcome. Therefore, the goal of this study was to determine the effects of these combined stressful abiotic conditions on maize grain yield and quality and to determine the genetic-specific response of maize genotypes in such conditions. The experiment was set up in eastern Croatia according to the randomized complete block design in four replications. A total of 10 maize hybrids of different FAO maturity groups were evaluated across four diverse environments, each subjected to one or two abiotic stresses (extreme precipitation, drought, high air temperatures, and acidic soil). Analysis of variance revealed that all treatment effects were statistically significant, except for the effect of hybrids on grain yield. Depending on the effect of abiotic stress, the variations among environments were up to 51.4% for yield and up to 12.1%, 18.9%, and 0.81% for protein, oil, and starch content, respectively. Differences among hybrids were less pronounced for yield (7.9%), while for protein (13.5%), oil (17.3%), and starch content (1.5%) were similar. However, the largest variation was found for the interaction effect. In the conducted research, ENV2 recorded the highest grain yield, along with the highest oil and starch content, as well as the second-highest protein content, while the hybrid effect remained unclear. Generally, ENV4 had the greatest negative impact due to the combined effects of extreme abiotic stresses, including soil acidity, drought, and high air temperatures. Full article