Agronomy

3 pages, 211 KB

Open AccessEditorial

System Resilience in Plant Stress Responses: From Memory Shaping to Transgenerational Adaptation

by Panfeng Yao

Agronomy 2025, 15(11), 2550; https://doi.org/10.3390/agronomy15112550 (registering DOI) - 2 Nov 2025

Traditional research in plant stress biology has predominantly focused on physiological and molecular response mechanisms under single stressors [...] Full article

(This article belongs to the Special Issue Resistance-Related Gene Mining and Genetic Improvement in Crops)

23 pages, 9926 KB

Open AccessReview

Research Trends in Evaluation of Crop Water Use Efficiency in China: A Bibliometric Analysis

by Tianci Wang, Yutong Xiao, Jiongchang Zhao and Di Wang

Agronomy 2025, 15(11), 2549; https://doi.org/10.3390/agronomy15112549 (registering DOI) - 1 Nov 2025

Abstract

Water scarcity has become a significant constraint to agricultural development in China. In this study, we employed bibliometric methods to systematically review the current research on crop water use efficiency (WUE) and the development trends in the North China Plain (NCP) and Northwest [...] Read more.

Water scarcity has become a significant constraint to agricultural development in China. In this study, we employed bibliometric methods to systematically review the current research on crop water use efficiency (WUE) and the development trends in the North China Plain (NCP) and Northwest China (NWC). We analyzed 1569 articles (NCP = 788; NWC = 781) from the Web of Science Core Collection (1995–2025) using visualization tools such as CiteSpace and VOSviewer to investigate annual numbers of publications, leading scholars and research institutions, and then to map keyword co-occurrence and co-citation structures. Our results showed that keyword clustering exhibited high structural quality (NCP: Q = 0.7345, S = 0.8634; NWC: Q = 0.758, S = 0.8912), supporting reliable thematic interpretation. The bibliometric analysis indicates a steady growth in annual publications since 1995, with the Chinese Academy of Sciences and China Agricultural University as leading contributors. From 1995 to 2005, studies centered on irrigation, yield and field-scale WUE, emphasizing the optimization of irrigation strategies and crop productivity. During 2006–2015, the thematic focus has broadened to encompass nitrogen use efficiency, crop quality and eco-environmental performance, thereby moving toward integrated evaluation frameworks that capture ecological synergies. Since 2016, the literature now emphasizes system integration, regional adaptability, climate-response mechanisms and the ecological co-benefits of agricultural practices. Future studies are expected to incorporate indicators such as crop quality, water footprint and carbon isotope indicators to support the sustainable development of agricultural water use. This study offers insights and recommendations for developing a comprehensive crop WUE evaluation framework in China, which will support sustainable agricultural water management and the realization of national “dual carbon” targets. Full article

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18 pages, 2009 KB

Open AccessArticle

Agronomic and Intercropping Performance of Newly Developed Elite Cowpea Lines for the West African Savannas

by Lucky Osabuohien Omoigui, Alpha Yaya Kamara, Abdulwahab Saliu Shaibu, Teryima Iorlamen, Godspower Ekeruo, Osagie Bright Eseigbe, Reuben Solomon, Musibau Abiodun Adeleke, Abdullahi Ibrahim Tofa and Esther Afor Ibrahim

Agronomy 2025, 15(11), 2548; https://doi.org/10.3390/agronomy15112548 (registering DOI) - 1 Nov 2025

Abstract

Cowpea production in Nigeria, the world’s largest producer, is insufficient to meet domestic demand due to significant yield gaps caused by various production constraints. Several high-yielding improved cowpea varieties have been developed and disseminated among smallholder farmers to improve productivity, but their adoption [...] Read more.

Cowpea production in Nigeria, the world’s largest producer, is insufficient to meet domestic demand due to significant yield gaps caused by various production constraints. Several high-yielding improved cowpea varieties have been developed and disseminated among smallholder farmers to improve productivity, but their adoption is low because breeding efforts have not adequately incorporated farmers’ and consumers’ preferred traits. To address this, a study was conducted to evaluate the performance of newly developed cowpea lines and identify those with traits preferred by farmers and consumers. Twenty-four cowpea lines were evaluated in multiple environments under sole and intercropped systems in Nigeria. The study revealed significant (p < 0.001) genotypic and genotype-by-environment interaction effects for grain yield, fodder yield, and other key agronomic traits. Three genotypes consistently outperformed the standard check, with UAM15-2157-4 exhibiting a 57.6% higher grain yield and superior seed quality. UAM15-2157-4 produced the highest grain yield (1289 kg ha⁻¹) under the intercropping system. GGE biplot analysis identified UAM15-2157-4 as the most stable genotype across all tested environments. This genotype, along with other promising lines, possesses desirable traits such as Striga resistance, large seed size, and preferred seed coat color, making them suitable for release and adoption to improve cowpea productivity in the region. Full article

(This article belongs to the Special Issue Trade-Offs in Crop Production: Yield and Quality, Resource Use Efficiency, and Environmental Sustainability)

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14 pages, 2343 KB

Open AccessArticle

Effect of Sowing Date and Low-Temperature Seed Germination on Rapeseed Yield

by Jifeng Zhu, Lei Lei, Xianmin Meng, Hongwei Li and Weirong Wang

Agronomy 2025, 15(11), 2545; https://doi.org/10.3390/agronomy15112545 (registering DOI) - 1 Nov 2025

Abstract

Direct seeding of winter rapeseed in the Yangtze River Basin often coincides with low temperatures during establishment. The aim of this study was to test whether low-temperature (LT) germination performance predicts overwintering survival and yield under delayed sowing. Thirty accessions were evaluated in [...] Read more.

Direct seeding of winter rapeseed in the Yangtze River Basin often coincides with low temperatures during establishment. The aim of this study was to test whether low-temperature (LT) germination performance predicts overwintering survival and yield under delayed sowing. Thirty accessions were evaluated in controlled germination at 20/14 °C (CK) and 12/6 °C (LT) and in two field seasons (2020–2021 and 2021–2022), with six sowing dates from 15 October to 4 December. Mean germination rate was 97.6% in CK and 88.0% in LT. Germination potential (GP) averaged 95.7% in CK and 41.9% in LT. Root and shoot length decreased from 7.63 and 5.02 cm in CK to 1.47 and 0.48 cm in LT. Overwintering survival declined with later sowing. In the colder season (2020–2021), survival for sowings after November fell below 20% for most accessions, whereas S1–S2 averaged above 80%. Yield decreased with delay. In 2021–2022, yield under S1 exceeded S2–S6 by 5.5%, 8.5%, 13.9%, 14.0%, and 23.3%. In 2020–2021, S1 was similar to S2, but 6.3–22.8% higher than S3–S6. Thousand-seed weight followed the same trend. LT GP and LT root length were positively correlated with yield at several sowing dates in the colder season, indicating that LT germination traits are predictive of late-sown performance under harsher winters. Seven accessions (3409, M417, Zheza0903, 86155, 3445, Zheyou50, and 3462) showed superior LT germination and comparatively better field performance. For the lower Yangtze site, a practical latest safe sowing window is late October, based on two seasons; November sowing substantially increases winter mortality and yield risk. Selecting genotypes with strong LT germination and managing for rapid autumn establishment can stabilize 1000-seed weight and yield when sowing is delayed. Full article

(This article belongs to the Section Horticultural and Floricultural Crops)

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33 pages, 3466 KB

Open AccessArticle

The Effect of Pre-Sowing Seed Treatment and Foliar Applications of Growth Stimulants on the Productivity of Perennial Grasses Under the Conditions of Northern Kazakhstan

by Saltanat Baidalina, Zhanat Salikova, Akhama Akhet, Ildar Bogapov, Miras Suraganov, Adiya Akhetova, Zhuldyz Alshinbayeva and Marden Baidalin

Agronomy 2025, 15(11), 2547; https://doi.org/10.3390/agronomy15112547 (registering DOI) - 31 Oct 2025

Abstract

A two-year (2023–2024) multifactorial field study was conducted under the agro-climatic conditions of Northern Kazakhstan, with the objective of refining cultivation practices for hayfields of perennial legumes and grasses, including alfalfa (Medicago sativa L.), smooth brome (Bromus inermis Leyss.), and sainfoin [...] Read more.

A two-year (2023–2024) multifactorial field study was conducted under the agro-climatic conditions of Northern Kazakhstan, with the objective of refining cultivation practices for hayfields of perennial legumes and grasses, including alfalfa (Medicago sativa L.), smooth brome (Bromus inermis Leyss.), and sainfoin (Onobrychis arenaria Kit). The elements targeted for optimization included the species composition and component ratios in the mixtures, as well as the regimes of pre-sowing and foliar applications of growth regulators (AminoMax, Black Jak, Miller Start, Lider-S). The integrated experimental design accounted for laboratory and field germination, biometric parameters (plant height, leafiness), phenophase dynamics, autumn survival and overwintering, indicators of photosynthetic activity, as well as yields of green biomass and dry matter, and chemical composition (crude protein, fiber, ash, fat, and nitrogen-free extract). Grass–legume mixtures ensured more stable progression of phenophases, improved overwintering, and enhanced protein value compared to monocultures; the inclusion of sainfoin contributed to improved forage quality without compromising yield. Growth regulators promoted accelerated initial plant development and enhanced the intensity of net photosynthetic productivity. The greatest effect of application was observed in the grass component with Miller Start, whereas in the legume species it was most pronounced with AminoMax. The results of the study revealed that the optimal proportion of legumes in the forage mixtures is 30–40%. Under contrasting hydrothermal conditions, the yield of fresh and dry matter ranged from 4.19 to 4.81 t ha⁻¹ and 1.27–1.51 t ha⁻¹ (2023) to 10.43–14.46 t ha⁻¹ and 3.05–4.63 t ha⁻¹ (2024). The greatest effect was observed with Miller Start and AminoMax treatments (p < 0.05), whereas the action of Black Jak and Lider-S was moderate, confirming differences in their mechanisms of action under contrasting weather conditions. Full article

(This article belongs to the Section Grassland and Pasture Science)

18 pages, 1513 KB

Open AccessArticle

Predicting Current and Future Potential Distributions of Ectropis grisescens (Lepidoptera: Geometridae) in China Based on the MaxEnt Model

by Cheng-Fei Song, Qing-Zhao Liu, Xin-Yao Ma, Jiao Liu and Fa-Lin He

Agronomy 2025, 15(11), 2546; https://doi.org/10.3390/agronomy15112546 (registering DOI) - 31 Oct 2025

Abstract

Ectropis grisescens Warren (Lepidoptera: Geometridae) is a destructive pest that has severely impacted major tea-growing regions in recent years; as such, it is vital to determine how climate change influences its areas of distribution. In this study, we employed a parameter-optimized maximum entropy [...] Read more.

Ectropis grisescens Warren (Lepidoptera: Geometridae) is a destructive pest that has severely impacted major tea-growing regions in recent years; as such, it is vital to determine how climate change influences its areas of distribution. In this study, we employed a parameter-optimized maximum entropy (MaxEnt) model, integrating 170 E. grisescens occurrence records and seven selected environmental variables, to predict the pest’s current and future potential distribution in China. Parameter optimization was conducted with the ENMeval package in R, identifying the optimal feature combination as “linear—L, quadratic—Q” and the regularization multiplier as 0.5. These results indicated that the mean diurnal range (bio2), precipitation of driest month (bio14), and elevation were the key variables contributing to the suitable area for E. grisescens. Currently, the total potential suitable area for E. grisescens in China spans approximately 1.969 × 10⁶ km², covering 20.51% of the country's land area, of which 5.121 × 10⁵ km², 7.385 × 10⁵ km², and 7.185 × 10⁵ km² possess low, medium, and high suitability, respectively. Notably, the high-suitability regions are predominantly concentrated in southeastern China, encompassing the provinces and municipalities of Zhejiang, Anhui, Hunan, Jiangsu, Chongqing, Jiangxi, Guangxi, Hubei, and Sichuan. Under future climate scenarios, it is projected that the suitable habitats for this pest will undergo varying degrees of change. Specifically, under the SSP1-2.6 scenario, the suitable habitat area is estimated to increase by up to 12.21% by the 2070s. Under the SSP2-4.5 scenario, the centroid of the suitable habitat will be displaced northwest by up to 238.4 km by the 2030s. Our findings provide valuable insights into the future management of E. grisescens and will aid in mitigating its ecological and economic impacts. Full article

(This article belongs to the Special Issue Sustainable Pest Management under Climate Change)

21 pages, 1206 KB

Open AccessArticle

Regulatory Effects of Different Compost Amendments on Soil Urease Kinetics, Thermodynamics, and Nutrient Stoichiometry in a Temperate Agroecosystem

by Qian Liu, Xu Zhang, Xingchi Guo, Ying Qu, Junyan Zheng, Yuhe Xing, Zhiyu Dong, Wei Yu, Guoyu Zhang and Pengbing Wu

Agronomy 2025, 15(11), 2544; https://doi.org/10.3390/agronomy15112544 (registering DOI) - 31 Oct 2025

Abstract

Compost amendments are widely recognized as an effective strategy for improving soil quality, modulating enzyme activities, and enhancing nitrogen cycling. Urease, a key enzyme in nitrogen transformation, is characterized by kinetic parameters such as the maximum reaction rate (V_max) and Michaelis [...] Read more.

Compost amendments are widely recognized as an effective strategy for improving soil quality, modulating enzyme activities, and enhancing nitrogen cycling. Urease, a key enzyme in nitrogen transformation, is characterized by kinetic parameters such as the maximum reaction rate (V_max) and Michaelis constant (K_m), as well as thermodynamic attributes including temperature sensitivity (Q₁₀), activation energy (E_a), enthalpy change (ΔH), Gibbs free energy change (ΔG), and entropy change (ΔS). However, how different compost sources regulate urease kinetics, thermodynamics, and nitrogen availability remains poorly understood. In this study, we evaluated the effects of three compost amendments—mushroom residue (MR), mushroom residue–straw mixture (MSM), and leaf litter (LL)—on urease kinetics and thermodynamics in a temperate agroecosystem. The MSM treatment significantly enhanced urea hydrolysis capacity and catalytic efficiency. In contrast, LL treatment resulted in the highest Km value, indicating a substantially lower enzyme-substrate affinity. Furthermore, MSM reduced the E_a and increased the thermal stability of urease, thereby supporting enzymatic performance under fluctuating temperatures. Collectively, our findings highlight that compost composition is a critical determinant of urease function and nitrogen turnover. By elucidating the coupled kinetic and thermodynamic responses of urease to compost inputs, this study provides mechanistic insights to guide optimized soil management and sustainable nitrogen utilization in temperate agricultural systems. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

19 pages, 2397 KB

Open AccessArticle

Spatial Distribution and Pollution Source Analysis of Heavy Metals in Cultivated Soil in Ningxia

by Xiang Yue, Rongguang Shi, Jianjun Ma, Hong Li, Tiantian Ma, Junhua Ma, Xiangyu Liang and Cheng Ma

Agronomy 2025, 15(11), 2543; https://doi.org/10.3390/agronomy15112543 (registering DOI) - 31 Oct 2025

Abstract

This study collected 820 topsoil samples from cultivated lands across Ningxia, covering the Yellow River irrigation area, the central arid zone, and the southern mountainous region. The ordinary kriging were spatially interpolated to analyze As, Hg, Cd, Cr, and Pb heavy-metal pollution spatial [...] Read more.

This study collected 820 topsoil samples from cultivated lands across Ningxia, covering the Yellow River irrigation area, the central arid zone, and the southern mountainous region. The ordinary kriging were spatially interpolated to analyze As, Hg, Cd, Cr, and Pb heavy-metal pollution spatial patterns. Pollution was evaluated using the Nemerow and geoaccumulation (I_(geo)) indices, and sources quantified via Pearson correlations, PCA (Principal Component Analysis), and PMF (Positive Matrix Factorization). The results indicated that Hg and Cd posed the highest ecological risks. The overall mean concentrations (mg.kg⁻¹) of Hg, Cd, As, Pb, and Cr were 0.04, 0.27, 9.91,23.81, and 57.34, respectively. Compared with the background values, they were 1.90, 2.41, 0.83, 1.14, 2.74 times higher, respectively. Geospatially, regions with higher pollution probabilities for Cd, Cr, Pb, Hg, and As were concentrated in the northern and central parts of Ningxia, whereas the southern region exhibited lower pollution probabilities. pH significantly influenced the accumulation and spatial distribution of heavy metals in soil. Source apportionment identified three primary contributors: transportation and natural parent materials (As, Pb, Cr), industrial activities (Hg), and agricultural practices (Cd). Hg and Cd were identified as the key risk elements requiring prioritized management. These results enhance understanding of the pollution levers of heavy metals in Ningxia cultivated soils, and also provide foundation for developing more scientific and precise soil risk control policies, offering significant practical value for environmental risk management. Full article

(This article belongs to the Special Issue Risk Assessment of Heavy Metal Pollution in Farmland Soil)

30 pages, 2006 KB

Open AccessArticle

Multi-Layer Soil Moisture Profiling Based on BKA-CNN by Integrating Sentinel-1/2 SAR and Multispectral Data

by Menglong Jiao, Xuqing Li, Xiao Sun, Jianjun Wu, Tianjie Zhao, Ruiyin Tang and Yu Bai

Agronomy 2025, 15(11), 2542; https://doi.org/10.3390/agronomy15112542 (registering DOI) - 31 Oct 2025

Abstract

Soil moisture (SM) is crucial for ecosystems and agriculture. Since the root systems of plants absorb water at different depths with different intensities, monitoring multi-layer SM can better respond to the water demand of plants and offer a crucial technical backing for drought [...] Read more.

Soil moisture (SM) is crucial for ecosystems and agriculture. Since the root systems of plants absorb water at different depths with different intensities, monitoring multi-layer SM can better respond to the water demand of plants and offer a crucial technical backing for drought monitoring and precision irrigation. Synthetic aperture radar (SAR) and multispectral (MS) have been widely used in SM estimation; however, their combined application for multi-layer SM profiling remains underexplored. Existing research based on these two data types has primarily focused on surface soil moisture (SSM), with limited investigation into estimating SM at deeper or varying depths. Therefore, the aims of this research are to integrate Sentinel-1 SAR and Sentinel-2 MS data and employ machine learning algorithms to estimate multi-layer SM in the Shandian River Basin. The results showed that (1) MS + SAR-based SM estimation significantly outperformed single-source data (MS or SAR alone). Specifically, MS data performed better in the root-zone estimation, while SAR data showed superior performance in SSM estimation. (2) The BKA-CNN estimation accuracy significantly outperformed RF and XGBoost. The results of its five-fold cross-validation are as follows: R² = 0.768 ± 0.011 at 3 cm, R² = 0.777 ± 0.013 at 5 cm, R² = 0.799 ± 0.011 at 10 cm, R² = 0.792 ± 0.01 at 20 cm, and R² = 0.782 ± 0.011 at 50 cm. (3) The BKA-CNN model performed better in grassland than in farmland. These findings indicate that the BKA-CNN model proposed in this study effectively improves the estimation precision of multi-layer SM by fusing SAR and MS data, demonstrating considerable generalization ability and robustness. It holds potential application value in ecological protection and agricultural water resource management. Full article

(This article belongs to the Section Water Use and Irrigation)

14 pages, 3183 KB

Open AccessArticle

A Spreading-Stem-Growth Mutation in Lolium perenne: A New Genetic Resource for Turf Phenotypes

by Izolda Pašakinskienė

Agronomy 2025, 15(11), 2541; https://doi.org/10.3390/agronomy15112541 (registering DOI) - 31 Oct 2025

Abstract

In Lolium perenne, a novel growth habit mutant, named VIROIZ, was recovered following colchicine treatment, and it was confirmed to maintain the diploid chromosome number (2n = 2x = 14). The mutation affected the stem morphology by inducing prolific [...] Read more.

In Lolium perenne, a novel growth habit mutant, named VIROIZ, was recovered following colchicine treatment, and it was confirmed to maintain the diploid chromosome number (2n = 2x = 14). The mutation affected the stem morphology by inducing prolific axillary shoot formation at nodal zones, resulting in a spreading growth habit that can extend to ~70 cm in width. Inheritance analysis based on single-plant evaluations in crosses with wild-type plants (F₁, n = 285; F₂, n = 380) and in selfed progeny (S₁, n = 255) consistently showed ~40% expression of the spreading phenotype, deviating from classical Mendelian ratios and indicating complex genetic control. Phenotypic selection further distinguished divergent classes: positively selected lines (C1⁺) averaged 3.90 axillary tillers per stem, whereas negatively selected lines (C1⁻) averaged only 0.22. Partial sequencing of 11 candidate genes implicated in shoot architecture, covering 40–90% of full-length DNA, did not provide a conclusive explanation for the altered stem growth. Notably, a single point mutation was observed in CRT3 (an endoplasmic reticulum chaperone that interacts with brassinosteroid signaling) highlighting it as a primary target for future studies. Cytological analysis of meiosis in F₁ hybrids between VIROIZ and wild-type plants revealed irregular chromosome pairing with persistent univalents (2–4 per cell), supporting the presence of structural chromosomal rearrangements that may disrupt gene organization and function in VIROIZ. The non-Mendelian segregation of the spreading phenotype, together with the observed meiotic irregularities, suggests that the mutation affects regulatory genes responsive to hormonal signals controlling axillary meristem initiation. The mutant represents a valuable resource for turf-type L. perenne breeding and for studying hormonal regulation of shoot morphogenesis in Poaceae. Full article

(This article belongs to the Special Issue New Strategies for Forage Breeding and Cultivation Under Challenging Conditions)

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17 pages, 4746 KB

Open AccessArticle

Effect of Silver Nanoparticles on Growth of Wheat: Is It Stage-Specific or Not?

by Alexander G. Khina, Liliya R. Biktasheva, Alexander S. Gordeev, Dmitry M. Mikhaylov, Maria T. Mukhina, Georgii V. Lisichkin and Yurii A. Krutyakov

Agronomy 2025, 15(11), 2540; https://doi.org/10.3390/agronomy15112540 (registering DOI) - 31 Oct 2025

Abstract

Experimental studies published to date on the effects of silver nanoparticles (AgNPs) on plants have yielded highly contradictory results: reported outcomes range from growth inhibition to stimulation. The objective of this research was to test the hypothesis that the ontogenetic stage at the [...] Read more.

Experimental studies published to date on the effects of silver nanoparticles (AgNPs) on plants have yielded highly contradictory results: reported outcomes range from growth inhibition to stimulation. The objective of this research was to test the hypothesis that the ontogenetic stage at the time of exposure to AgNPs is a key determinant of both the qualitative profile and quantitative magnitude of plant responses. For this purpose, laboratory seed priming and small-plot field experiments with wheat plants (Triticum aestivum L.) treated with stabilized dispersions of AgNPs at 1–100 mg∙L⁻¹ were conducted. It was shown that seed priming with low concentrations of AgNPs (1–5 mg∙L⁻¹) did not affect wheat seedling growth, whereas dispersions at ≥25 mg∙L⁻¹ suppressed development. In agreement, antioxidant enzyme activities (POD, CAT, PPO) increased at 1–5 mg·L⁻¹ and decreased at 100 mg·L⁻¹. By contrast, foliar treatments of field-grown wheat increased plant population density, plant height, spike structure metrics, and grain yield. The optimal regimen—three foliar applications at 5 mg·L⁻¹—increased grain yield by 12.1% from 5.89 t·ha⁻¹ to 6.60 t·ha⁻¹. At low doses of AgNPs, activities of peroxidase, catalase, and polyphenol oxidase in seedlings tissues increased, indicating activation of nonspecific defense mechanisms; at higher concentrations, activities of these enzymes decreased, indicating antioxidant system exhaustion and dysfunction. The findings demonstrate dose- and stage-dependent effects and corroborate the central role of the developmental stage of wheat in determining responses to AgNPs, indicating opportunities to optimize stage-aware, low-dose application regimes to enhance productivity while minimizing phytotoxic risk. Full article

(This article belongs to the Section Plant-Crop Biology and Biochemistry)

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16 pages, 3962 KB

Open AccessArticle

Biochar Application Enhances Soil Carbon Sequestration in the North China Plain by Improving Soil Properties and Reshaping Microbial Community Structure

by Yang Wang, Mengping Zhang, Ao Sun, Xin Fu, Zhengping Peng, Huasen Xu and Cheng Xue

Agronomy 2025, 15(11), 2539; https://doi.org/10.3390/agronomy15112539 (registering DOI) - 31 Oct 2025

Abstract

Biochar amendment has been widely recognized for its potential to promote soil carbon sequestration and improve crop productivity; however, the microbial mechanisms underlying carbon sequestration at varying biochar application rates remain insufficiently understood. In this study, a field experiment was conducted in a [...] Read more.

Biochar amendment has been widely recognized for its potential to promote soil carbon sequestration and improve crop productivity; however, the microbial mechanisms underlying carbon sequestration at varying biochar application rates remain insufficiently understood. In this study, a field experiment was conducted in a typical fluvo-aquic soil region of the North China Plain under a maize–wheat rotation, with one-time biochar application at four levels: CK (0 t ha⁻¹), B5 (5 t ha⁻¹), B10 (10 t ha⁻¹), and B20 (20 t ha⁻¹). The effects of these treatments on soil physicochemical properties, organic carbon fractions, microbial community structure, and enzyme activities were systematically examined. The results showed that soil total nitrogen (TN) and pH increased consistently with higher biochar application rates, reaching maximum values under B20 treatment, where TN and pH rose by 35.56% and 7.00% relative to CK, respectively. In contrast, the contents of NH₄⁺-N, available phosphorus (AP), and available potassium were mostly enhanced under B5 during the maize season, while in the wheat season, NH₄⁺-N peaked under B10 and AP peaked under B5. Biochar addition significantly increased soil organic carbon fractions and the carbon pool management index (CMI). In the maize season, soil organic carbon (SOC), microbial biomass carbon (MBC), particulate organic carbon (POC), and CMI under B20 rose by 55.99%, 39.67%, 79.69% and 180.54% over CK, respectively, whereas dissolved organic carbon (DOC) peaked under B5. Throughout the wheat season, SOC, MBC, and POC contents under B20 were 53.70%, 64.31% and 147.81% higher than CK, while DOC peaked under B5 (+56.98%). Soil enzyme activities, including catalase, urease, invertase and alkaline phosphatase, were strongly stimulated by biochar, with B20 increasing their activities by 4.49–18.18%, 3.19–19.77%, 6.14–26.14% and 12.25–33.19%, respectively. Biochar also reshaped microbial community structure: the during maize season, it reduced the relative abundance of Glomeromycetes (65.31%) and Oligohymenophorea (51.64%) while enhancing Deltaproteobacteria (46.15%) and Gammaproteobacteria (29.03%); during wheat season; it enhanced Eurotiomycetes (85.77%) and Dothideomycetes (16.28%) but suppressed Deinococci (74.08%) and Alphaproteobacteria (4.39%). Pathway analysis further indicated that biochar amendments indirectly increased SOC fractions and CMI by simultaneously altering nutrient availability, regulating microbial community structure, and stimulating soil enzyme activities. Collectively, these findings highlight that the effects of biochar are dosage-specific: moderate rates (e.g., 5 t ha ⁻¹) are more suitable for the short-term improvement of soil fertility, while higher rates (e.g., 20 t ha⁻¹) are more effective for long-term carbon sequestration; depending on the objective, biochar application can thus substantially modify soil physicochemical and biological processes to promote agroecosystem sustainability in the North China Plain. Full article

(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)

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5 pages, 162 KB

Open AccessEditorial

Advances in Tillage Methods to Improve the Yield and Quality of Crops

by Jiatun Xu and Xiaobo Gu

Agronomy 2025, 15(11), 2538; https://doi.org/10.3390/agronomy15112538 (registering DOI) - 31 Oct 2025

Abstract

Against the backdrop of intensifying global climate change, continuous population growth, and increasing pressure on natural resources and the environment, traditional extensive agricultural practices are no longer sustainable [...] Full article

(This article belongs to the Special Issue Advances in Tillage Methods to Improve the Yield and Quality of Crops)

20 pages, 5671 KB

Open AccessArticle

Quantifying Grazing Intensity from Aboveground Biomass Differences Using Satellite Data and Machine Learning

by Ritu Su, Yong Yang, Shujuan Chang, Gudamu A, Xiangjun Yun, Xiangyang Song and Aijun Liu

Agronomy 2025, 15(11), 2537; https://doi.org/10.3390/agronomy15112537 (registering DOI) - 31 Oct 2025

Abstract

Accurately quantifying grazing intensity (GI) is crucial for assessing grassland utilization and supporting sustainable management. Traditional livestock-based approaches cannot capture the spatial heterogeneity of grazing or its dynamic response to climate variability. The objective of this study was to develop a remote sensing-based [...] Read more.

Accurately quantifying grazing intensity (GI) is crucial for assessing grassland utilization and supporting sustainable management. Traditional livestock-based approaches cannot capture the spatial heterogeneity of grazing or its dynamic response to climate variability. The objective of this study was to develop a remote sensing-based quantitative framework for estimating GI across the Inner Mongolian grasslands. The framework integrates MODIS vegetation indices, ERA5-Land climate variables, topographic factors, and field-measured data and GI was quantified as the proportional difference between potential and satellite-derived aboveground biomass (AGB), providing a spatially explicit measure of forage utilization. In this framework, potential AGB (AGBp) represents the climate-driven growth capacity under ungrazed conditions reconstructed using machine learning models, whereas satellite-derived AGB (AGBs) denotes the standing AGB remaining under current grazing pressure. Validation using 324 paired grazed–ungrazed plots demonstrated strong agreement between modeled and observed GI (R² = 0.65, RMSE = 0.18). This AGB-difference-based approach provides an effective and scalable tool for large-scale rangeland monitoring, offering quantitative insights into grass–livestock balance, ecological restoration, and adaptive management in arid and semi-arid regions. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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15 pages, 1126 KB

Open AccessArticle

The Influence of Foliar Application of Nod Factors (LCOs) and Microelements on the Growth, Development, and Yield of Peas (Pisum sativum L.)

by Janusz Podleśny, Jerzy Wielbo, Anna Podleśna, Hanna Klikocka and Dominika Kidaj

Agronomy 2025, 15(11), 2536; https://doi.org/10.3390/agronomy15112536 (registering DOI) - 31 Oct 2025

Abstract

Peas are a popular crop grown in Poland, but their yields are variable and often low; therefore, new cultivation methods are constantly being sought. In this paper, we present the results of a three-year greenhouse study examining the effect of preparations containing rhizobial [...] Read more.

Peas are a popular crop grown in Poland, but their yields are variable and often low; therefore, new cultivation methods are constantly being sought. In this paper, we present the results of a three-year greenhouse study examining the effect of preparations containing rhizobial Nod factors and/or selected microelements (B, Cu, Fe, Mn, Zn, and Mo) on the physiological parameters, growth, and yield of peas. Pea plants were tested at the flowering stage (BBCH 60), at the green ripe stage (BBCH 75), and at the fully ripe stage (BBCH 90). Leaf area, SPAD, gas exchange parameters, and chlorophyll fluorescence were measured, and the number and mass of root nodules, as well as seed yield and yield components, were determined. The treatment was most effective when Nod factors were used in combination with microelements. The increase in pea yield induced by the application of both components can be attributed to the higher number of pods and seeds per plant because no significant variations were noted in the number of seeds per pod and 1000 seed weight. The number and weight of nodules were significantly correlated with the pea yield, and the value of the correlation coefficients was influenced by the application of both components. Full article

(This article belongs to the Special Issue Crop Productivity and Management in Agricultural Systems)

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21 pages, 6047 KB

Open AccessArticle

Metagenomics Insights into the Functional Profiles of Soil Carbon, Nitrogen Under Long-Term Chemical and Humic Acid Urea Application

by Tengfei Guo, Mengyuan Wang, Ke Yue, Peng Luo, Xiao Song, Shaomin Huang, Xinpeng Xu, Qian Zhang, Yulu Chen, Zekun Zhang, Sumiao Su, Shijie Ding, Shuiqing Zhang and Kun Zhou

Agronomy 2025, 15(11), 2535; https://doi.org/10.3390/agronomy15112535 - 30 Oct 2025

Abstract

Long-term positioning tests can systematically reveal the evolution characteristics of soil fertility and crop productivity, and reflect the spatiotemporal changes in soil quality and their driving factors. While soil microorganisms mediating nutrient cycling are crucial for maintaining crop productivity and the long-term resilience [...] Read more.

Long-term positioning tests can systematically reveal the evolution characteristics of soil fertility and crop productivity, and reflect the spatiotemporal changes in soil quality and their driving factors. While soil microorganisms mediating nutrient cycling are crucial for maintaining crop productivity and the long-term resilience of agricultural ecosystems, how prolonged use of different fertilization strategies affects their functional capacity remains insufficiently understood. In this study, we applied metagenomic sequencing to investigate how three fertilization treatments, namely (i) N0 receiving only phosphorus (P) and potassium (K) fertilizers, (ii) N250 receiving conventional urea + P and K, and (iii) F250 receiving humic acid urea + P and K, influence soil microbial communities, functional genes related to C and N cycling, and associated soil properties in a long-term field experiment. The F250 treatment significantly increased average annual yields of wheat and maize to 7166.21 kg hm⁻² and 8309.96 kg hm⁻², respectively. These values were 148.66% and 73.47% higher than those under N0, and 8.22% and 11.64% higher than those under N250. Compared with N0, both N250 and F250 signally augmented soil nitrate, ammonium, total nitrogen (TN), and soil organic carbon (SOC), altered microbial community composition, and enhanced the relative abundance of genes engaged in C fixation and methane oxidation. Both treatments also promoted denitrification and dissimilatory nitrate reduction to ammonium (DNRA). Relative to N250, F250 specifically enriched the beneficial bacterial genus Pedobacter, further increased the abundance of the C fixation gene pccA, and markedly upregulated the DNRA gene nrfA. Soil TN and SOC were identified as the key environmental factors regulating microbial community structure and the functional potential of C and N cycling pathways. Collectively, our findings provide a mechanistic understanding of how long-term application of humic acid urea enhances crop productivity by modulating the genetic potential of soil microorganisms in biogeochemical cycles, offering a biological foundation for optimizing fertilization strategies in sustainable agriculture. Full article

(This article belongs to the Section Soil and Plant Nutrition)

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22 pages, 5381 KB

Open AccessArticle

Multi-Scale Multi-Branch Convolutional Neural Network on Google Earth Engine for Root-Zone Soil Salinity Retrieval in Arid Agricultural Areas

by Wenli Dong, Xinjun Wang, Songrui Ning, Wanzhi Zhou, Shenghan Gao, Chenyu Li, Yu Huang, Luan Dong and Jiandong Sheng

Agronomy 2025, 15(11), 2534; https://doi.org/10.3390/agronomy15112534 - 30 Oct 2025

Abstract

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional [...] Read more.

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R² = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m⁻¹, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties. Full article

(This article belongs to the Section Farming Sustainability)

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19 pages, 1190 KB

Open AccessArticle

Effects of Allelic Variation in Storage Protein Genes on Seed Composition and Agronomic Traits of Soybean in the Omsk Oblast of Western Siberia

by Ilya V. Strembovskiy, Pavel Yu. Kroupin, Lyudmila V. Omel’yanuk, Andrey V. Arkhipov, Yana S. Meglitskaya, Mikhail S. Bazhenov, Akimbek M. Asanov, Mariya E. Mukhordova, Oksana A. Yusova, Yuliya I. Yaschenko, Gennady I. Karlov and Mikhail G. Divashuk

Agronomy 2025, 15(11), 2533; https://doi.org/10.3390/agronomy15112533 - 30 Oct 2025

Abstract

Developing soy cultivars for northern long-day regions requires understanding how alleles of protein accumulation genes function in non-optimal environments like Western Siberia, where their effects may diverge from those established in other regions. We hypothesized that allelic variation in the genes GmSWEET39, [...] Read more.

Developing soy cultivars for northern long-day regions requires understanding how alleles of protein accumulation genes function in non-optimal environments like Western Siberia, where their effects may diverge from those established in other regions. We hypothesized that allelic variation in the genes GmSWEET39, Glyma.03G219900, Glyma.14G119000, Glyma.17G074400, and POWR1 would have measurable and predictable effects on seed composition and plant architecture in soybean, even under the stressful long-day conditions of Western Siberia (Omsk Oblast). Over a three-year period (2021–2023), a diverse collection of 58 soybean accessions was phenotyped for yield-related traits and genotyped using established KASP and PCR markers and a novel KASP marker for GmSWEET39. Our results demonstrate that the GmSWEET39 CC+ allele is significantly associated with an increase in seed protein content by up to 1.9 pp, a decrease in seed oil content up to 1.4 pp, and a reduction in plant height by up to 20%, while the Glyma.17G074400 SNP(T) allele was associated with an increase in oil content up to 1.4 pp. Strong negative correlations were found between protein content and plant height, whereas plant height was positively correlated with flowering time. Broad-sense heritability was high (H² > 0.82) for all traits except fiber content. The genotypic structure of the collection revealed a predominance of oil-favoring alleles, with rare protein-enhancing alleles identified in accessions from Sweden, Poland, China, and Japan. These accessions have been proposed as valuable donors for breeding. This study validates the utility of marker-assisted selection for the development of high-protein and high-oil soybean varieties tailored to the challenging photoperiod and climatic conditions of northern regions. Full article

(This article belongs to the Section Crop Breeding and Genetics)

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21 pages, 5438 KB

Open AccessArticle

Study on the Improved Black Soil Structure Under Biological Tillage on Brassica chinensis L. Yield

by Baoguang Wu, Pu Chen, Zhipeng Yin, Shun Xu, Yuping Liu, Qiuju Wang, Zhenyu Wang and Junting Ye

Agronomy 2025, 15(11), 2532; https://doi.org/10.3390/agronomy15112532 - 30 Oct 2025

Abstract

The degradation of soil structure in black soils has become a key factor limiting the productivity of farmland ecosystems. However, systematic studies on restoring soil physical properties and improving crop yields through biological tillage remain scarce. In this study, Eisenia fetida was employed [...] Read more.

The degradation of soil structure in black soils has become a key factor limiting the productivity of farmland ecosystems. However, systematic studies on restoring soil physical properties and improving crop yields through biological tillage remain scarce. In this study, Eisenia fetida was employed as a biological tillage agent to create soil macropores. An orthogonal experiment with three factors was conducted to investigate the mechanisms by which different gradients of soil moisture, decomposed straw, and soil compaction affect soil pore structure and the yield of Brassica chinensis L. X-ray-computed tomography (CT) was used to quantitatively characterize the macropore network mediated by earthworms. The results indicated that the critical threshold conditions for optimized biological tillage were 50 g of decomposed straw, a compaction of 50–150 kPa, and a soil moisture content of 30–37%. Under these conditions, earthworm activity significantly enhanced the leaf dry weight of Brassica chinensis L. by approximately 55.29%, while root dry weight increased by 96.60%. Compared with treatments of low soil moisture combined with 50 g of decomposed straw, higher moisture levels further increased total biomass by 75.46%. Compared with the control, earthworm-induced macropores had 27 times more pore throats than abiotic pores, and network models showed significantly improved connectivity, indicating enhanced soil structure. This study revealed a synergistic threshold of water–food–physical resistance regulation for soil structural improvement under biological tillage and innovatively proposed a biological tillage evaluation system based on CT-quantified pore networks and root structure–function relationships. These findings provide a theoretical basis for the ecological restoration of degraded black soils. Full article

(This article belongs to the Section Farming Sustainability)

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