Agronomy

12 pages, 2192 KB

Open AccessArticle

Plant Residue Input Enhances Soil Multifunctionality by Reshaping Microbial Communities in Saline–Alkali Soil of Northeast China

by Jie Song, Yibo Wang, Changjiang Zhao, Yan Sun, Qin Yao and Yuhu Zuo

Agronomy 2026, 16(14), 1307; https://doi.org/10.3390/agronomy16141307 (registering DOI) - 8 Jul 2026

Abstract

Soil microorganisms and plant residue decomposition are critical drivers of soil nutrient cycling and multifunctionality, yet their regulatory mechanisms in saline–alkali soils are not fully understood. This study selected bare land and forestland (shrub and tree stands) in Daqing, Heilongjiang, to investigate the [...] Read more.

Soil microorganisms and plant residue decomposition are critical drivers of soil nutrient cycling and multifunctionality, yet their regulatory mechanisms in saline–alkali soils are not fully understood. This study selected bare land and forestland (shrub and tree stands) in Daqing, Heilongjiang, to investigate the effects of plant residue input on forest soil properties, microbial communities, keystone taxa, and multifunctionality using high-throughput sequencing and multivariate analysis. Results showed that plant residue cover significantly improved soil nutrients (SOC, TN, TP, TK, AN), enhanced alkaline phosphatase activity, and increased soil multifunctionality compared with bare land. Plant residues also increased bacterial α-diversity and shifted community composition, with elevated relative abundances of Proteobacteria, Bacteroidota, Planctomycetota, Patescibacteria, and key genera (Mycobacterium, Pseudonocardia, Bryobacter, Steroidobacter). Non-metric multidimensional scaling (NMDS) and correlation analysis revealed microbial communities and keystone taxa were closely correlated with soil nutrients and multifunctionality. Overall, plant residues enhance forest soil multifunctionality by improving soil organic matter, optimizing microbial community structure, and stimulating keystone taxa, providing a scientific basis for understanding microbial-driven nutrient cycling and vegetation restoration in degraded saline–alkali soils. Full article

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15 pages, 1025 KB

Open AccessArticle

Effects of Shading on Grain Filling, Yield and Quality of Rice Noodle-Specific Cultivars

by Yang Shui, Peng Zhang, Guohao Zhang, Haixiao Xia, Taishen Wen, Hong Yu, Shan Wan, Guotao Yang and Shengmin Yan

Agronomy 2026, 16(14), 1306; https://doi.org/10.3390/agronomy16141306 (registering DOI) - 8 Jul 2026

Abstract

Low-light environments severely affect rice yield and quality. As an important raw material for rice noodles, improving rice yield and quality under low-light conditions is very important. In this study, the grain-filling dynamics, yield, processing quality, and appearance quality of two rice noodle-specific [...] Read more.

Low-light environments severely affect rice yield and quality. As an important raw material for rice noodles, improving rice yield and quality under low-light conditions is very important. In this study, the grain-filling dynamics, yield, processing quality, and appearance quality of two rice noodle-specific cultivars, i.e., Gui Chao II (GCII) and Guangyou 2928 (GY2928), were investigated under shading in Sichuan. At the experimental site, the soil type is loam, average temperature reaches 23.1 °C, total rainfall reaches 780–830 mm, and total sunshine duration is about 760 h. A two-factor split-plot design was conducted. Two light intensities (natural light vs. 51% shading) served as the main plot, and two rice cultivars (GCII and GY2928) served as the subplot, and each was replicated three times. Results showed that, compared with normal light, shading reduced the average filling rate (Vmean) of GCII and GY2928 by 20.67% and 18.69%, the maximum filling rate (Vmax) decreased by 20.26% and 16.18%, the 100-grain weight (Gmax) at the maximum filling rate decreased by 0.42 g and 0.21 g, and the active filling period (D) was shortened by 1.47 days and 4.01 days, respectively. Compared with normal light, the decreased grain filling led to a significant decrease in spikelet fertility by 30.62% and 28.50% for GCII and GY2928 under shading. Finally, the yield of GCII and GY2928 significantly decreased by 25.33% and 27.82% compared with normal light. Compared with normal light, shading increased the head rice rate of GCII, while that of GY2928 was significantly reduced. In contrast, the chalkiness rate and chalkiness degree of GCII under shading were significantly reduced, but those of GY2928 significantly increased. In conclusion, GCII exhibited more stable yield and quality under shading, and breeding and planting such varieties could increase rice yield and quality in low-light environments. Full article

(This article belongs to the Section Farming Sustainability)

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20 pages, 2042 KB

Open AccessArticle

Improving Maize Drought Tolerance Under a Continental Climate: A Sap-Flow-Based Evaluation of Biostimulants and Supplemental Irrigation in the Pannonian Basin

by Dávid Pásztor, Györgyi Kovács, Attila Nagy, Gift Siphiwe Nxumalo, Géza Tuba and János Tamás

Agronomy 2026, 16(14), 1305; https://doi.org/10.3390/agronomy16141305 (registering DOI) - 8 Jul 2026

Abstract

Maize (Zea mays L.) is the dominant cereal of continental Hungary, yet the Pannonian belt lost one-third of its planted area over the last decade (1150 kha to 770 kha in 2025). This study quantified how supplemental irrigation and biostimulants affect maize [...] Read more.

Maize (Zea mays L.) is the dominant cereal of continental Hungary, yet the Pannonian belt lost one-third of its planted area over the last decade (1150 kha to 770 kha in 2025). This study quantified how supplemental irrigation and biostimulants affect maize transpiration. Fourteen Dynamax Flow32-1K stem-heat-balance sensors recorded sap flow at 15 min resolution on the Sushi FAO 340 hybrid across seven irrigated–rainfed plot pairs at Karcag, Hungary. Measurements spanned a dry 2024 season (irrigation: 253 mm; precipitation: 7.9 mm; VPD_max: 1.71 kPa) and a wetter 2025 season (120 mm irrigation; 62.9 mm precipitation; mean VPD_max: 1.33 kPa). A Control-only mixed-effects model returned a year × irrigation interaction F(1, 84) = 106 (p < 10⁻¹⁵): irrigation raised transpiration by 77% in 2024 and lowered it by 12% in 2025. The VPD_max–transpiration coupling was inverted in 2024, the field signature of stomatal closure under soil-water limitation. The irrigated Big Compost plot reached a grain-based WUE of 97.5 kg mm⁻¹ versus 41.6 kg mm⁻¹ for the matched Control. This was a 2.3-fold within-2025 separation at similar per-plant transpiration. The irrigation response differed sharply between seasons. However, the amendment classes were tested in different years, and the irrigation dose differed between seasons (253 mm in 2024 versus 120 mm in 2025). The cross-class contrast is therefore exploratory, and every cross-year comparison is provisional. With one sensor per plot, the amendment ranking remains a hypothesis for a replicated, same-season, and same-dose follow-up. Full article

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

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15 pages, 1041 KB

Open AccessReview

A Review: Mechanisms, Control Strategies, and Future Perspectives of Apple Replant Disease in China

by Yang Cao, Long Li, Baisheng Ma, Quan Fang, Peihua Du and Yifeng Feng

Agronomy 2026, 16(14), 1304; https://doi.org/10.3390/agronomy16141304 (registering DOI) - 8 Jul 2026

Abstract

Apple (Malus domestica Borkh.) is a major fruit crop of global economic importance, and China ranks first worldwide in both apple cultivation area and total production. With the large-scale renewal of aging orchards, apple replant disease (ARD) has become increasingly prevalent in [...] Read more.

Apple (Malus domestica Borkh.) is a major fruit crop of global economic importance, and China ranks first worldwide in both apple cultivation area and total production. With the large-scale renewal of aging orchards, apple replant disease (ARD) has become increasingly prevalent in major apple-producing regions. ARD is typically characterized by severe growth suppression, impaired root development, increased incidence of soil-borne diseases, and, in severe cases, seedling mortality. These symptoms substantially constrain orchard renewal, limit improvements in fruit yield and quality, and threaten the sustainable development of the apple industry. The etiology of ARD is complex and involves the synergistic interaction of three factors: soil microbial dysbiosis characterized by pathogen enrichment and the depletion of beneficial microorganisms; allelopathic autotoxicity caused by the accumulation of phenolic acids, especially phloridzin; and degraded soil physicochemical properties, including acidification, compaction, and nutrient imbalance. Current management strategies mainly include the use of ARD-tolerant rootstocks, microbial regulation, chemical and physical soil disinfection, and agronomic practices such as crop rotation and organic amendment application. Among these approaches, biological regulation mediated by beneficial rhizosphere and endophytic microorganisms has attracted increasing attention because of its environmental compatibility and sustainability. This review summarizes the occurrence patterns, regional characteristics, core pathogenic mechanisms, and integrated management strategies of ARD, with particular emphasis on the functional roles of rhizosphere and endophytic microbiomes in disease alleviation. The review provides a theoretical basis and practical reference for the development of green, efficient, and sustainable strategies for ARD control and apple orchard management. Full article

(This article belongs to the Topic Interactions between Plants and Soil Microbes in Natural Ecosystem)

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21 pages, 4981 KB

Open AccessArticle

Anaerobic Co-Digestion of Polylactic Acid (PLA) Films with Organic Fraction of Municipal Solid Waste: Biodegradation, Biogas Yields, and Metabolomic Analysis

by Nicolò Montegiove, Debora Puglia, Roberto Maria Pellegrino, Franco Dominici, Eleonora Calzoni and Daniela Pezzolla

Agronomy 2026, 16(14), 1303; https://doi.org/10.3390/agronomy16141303 (registering DOI) - 8 Jul 2026

Abstract

The increasing use of bioplastics in packaging applications necessitates rigorous evaluation of their fate in real waste management systems. While bioplastics are often marketed as biodegradable, their actual behavior under mesophilic anaerobic digestion (AD) is frequently insufficiently understood and often overestimated in commercial [...] Read more.

The increasing use of bioplastics in packaging applications necessitates rigorous evaluation of their fate in real waste management systems. While bioplastics are often marketed as biodegradable, their actual behavior under mesophilic anaerobic digestion (AD) is frequently insufficiently understood and often overestimated in commercial claims. Polylactic acid (PLA), one of the most widely produced bio-based polymers, has been widely characterized under these conditions, but little is known about the metabolomic changes associated with its biodegradation under mesophilic anaerobic conditions. This study investigates the mesophilic AD (37 °C for more than 3 months) of PLA films (2.5 × 2.5 cm) co-digested with the organic fraction of municipal solid waste (OFMSW). Biogas production and energy yield evaluation were assessed for AD, along with chemical parameters and metabolomic analyses. PLA biodegradation, calculated according to ISO 15985:2014, reached values close to 100% after more than 3 months, highlighting a prolonged lag phase under mesophilic AD conditions. The biogas production yielded about 380 Nm³ per t of volatile solids. Metabolomic profiling during AD revealed that the onset of PLA biodegradation, highlighted also by biogas emission, coincides with the appearance of key metabolites associated with PLA hydrolysis. These findings demonstrate that the mesophilic anaerobic co-digestion of PLA films with OFMSW did not cause any inhibition effect on biogas production. The results demonstrate the feasibility of incorporating PLA into existing organic waste treatment systems, thereby supporting both energy recovery and sustainable waste management. Full article

(This article belongs to the Special Issue A Path for Circular Economy in Agriculture: From Organic Waste to Sustainable Energy and Soil Fertility)

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32 pages, 10063 KB

Open AccessArticle

Estimating Grassland Production in Central Europe Using Multi-Source Remote Sensing Data and a Novel Compilation of Field Observations

by Vivien Pacskó, Zoltán Barcza, János Balogh, Szabolcs Balogh, Márta Belényesi, Gianni Bellocchi, Edina Birinyi, Szilvia Fóti, Roland Hollós, Dániel Kristóf, György Kröel-Dulay, Zoltán Nagy, Gábor Ónodi, Róbert Pataki, Ottó Petrik, Krisztina Pintér, Mátyás Richter-Cserey, Máté Simon, Mirtill Tusjak, Gábor Timár and Anikó Kern Show full author list Hide full author list

Agronomy 2026, 16(14), 1302; https://doi.org/10.3390/agronomy16141302 (registering DOI) - 8 Jul 2026

Abstract

Monitoring the condition of grasslands is essential given their vital role in food security, carbon sequestration and other ecosystem services. Harvested aboveground biomass (HAB) and aboveground net primary production (ANPP) are among the most important grassland state indicators. However, spatially explicit production estimates [...] Read more.

Monitoring the condition of grasslands is essential given their vital role in food security, carbon sequestration and other ecosystem services. Harvested aboveground biomass (HAB) and aboveground net primary production (ANPP) are among the most important grassland state indicators. However, spatially explicit production estimates are largely lacking, and grassland area estimations also remain uncertain. This study addresses these gaps for drought-prone Central European grasslands over 2017–2024. We synthesized grassland extent data, collected extensive field measurements on biomass (BM), and used remote sensing-based biophysical proxies to build an ensemble of six linear models for spatial extrapolation at 10 m resolution. Bayesian framework was used for the linear model fitting that also considers uncertainty of the observations. The ensemble mean ANPP was 310.7 ± 19 gBM m⁻², with modest interannual variability. Upscaled country-wide mean ANPP was 34.3 ± 13.3 Mt year⁻¹. The results indicate that, within the frame of the present study, the remote sensing-based linear model selection has a larger influence on the country totals than the grassland area database selection. The results highlight that both grassland area uncertainty and model construction are major sources of uncertainty in biomass estimation that have to be addressed in future studies. Full article

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

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26 pages, 5673 KB

Open AccessArticle

Crop Water Footprints in the Manas River Basin: Trends, Drivers, and Futures

by Yongjun Du, Xiaolong Li, Xinlin He, Quanli Zong, Guang Yang, Muhammad Arsalan Farid and Zhengrong Wei

Agronomy 2026, 16(13), 1301; https://doi.org/10.3390/agronomy16131301 (registering DOI) - 7 Jul 2026

Abstract

The management and efficient use of water resources are crucial to the sustainable development of agriculture in arid regions. The Manas River Basin faces severe water shortages due to its arid climate and heavy reliance on irrigation water. Therefore, based on water footprint [...] Read more.

The management and efficient use of water resources are crucial to the sustainable development of agriculture in arid regions. The Manas River Basin faces severe water shortages due to its arid climate and heavy reliance on irrigation water. Therefore, based on water footprint theory, this study comprehensively utilized the CROPWAT model, pathway analysis, and CMIP6 data to construct an integrated “assessment–driving–prediction” framework for crop water footprints, with the aim of revealing the evolution patterns and driving mechanisms of water footprints in river basins. The results showed that the cultivated area of crops in the Manas River Basin exhibited a nonlinear expansion trend from 1990 to 2020, with a total increase of 143.56% over the 30-year period. Among all crops, cotton occupied the largest cultivated area, accounting for 60.34% of the total. During the study period, the crop water footprint, crop blue water footprint, and crop green water footprint in the Manas River Basin showed overall upward trends, increasing by 1.07 × 10⁹ m³, 1.04 × 10⁹ m³, and 3.0 × 10⁷ m³, respectively. Total agricultural machinery power and per capita grain production are the main factors influencing changes in crop water footprint. Under future climate scenarios, the crop water footprint in the Manas River Basin is projected to follow the order SSP2-4.5 > SSP5-8.5 > SSP1-2.6. By 2100, the crop water footprint under the SSP2-4.5 scenario is expected to increase by 37.01% relative to 2020, posing substantial challenges to agricultural water resource management in the basin. In contrast, the crop water footprint under the SSP1-2.6 scenario remains relatively stable, indicating a more sustainable development pathway. Full article

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

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20 pages, 811 KB

Open AccessArticle

Yield and Chemical Composition of Maize (Zea mays L.) Green Fodder Depending on Different Sowing Dates as an Element of Sustainable Agriculture

by Piotr Szulc, Katarzyna Ambroży-Deręgowska, Marek Selwet, Karolina Kolańska, Roman Wąsala and Krzysztof Górecki

Agronomy 2026, 16(13), 1300; https://doi.org/10.3390/agronomy16131300 - 7 Jul 2026

Abstract

The field study was conducted between 2016 and 2018 by the Department of Agronomy at Poznań University of Life Sciences. The experiment took place at the fields of the Research and Education Centre in Gorzyń, Złotniki branch. It was a single-factor trial involving [...] Read more.

The field study was conducted between 2016 and 2018 by the Department of Agronomy at Poznań University of Life Sciences. The experiment took place at the fields of the Research and Education Centre in Gorzyń, Złotniki branch. It was a single-factor trial involving six different sowing dates of an ultra-early maize cultivar: A1—12 April, A2—26 April, A3—10 May, A4—24 May, A5—7 June, and A6—21 June. The cultivar ‘Pyroxenia’ was used in the study. It is characterized by very early maturity (FAO 130), rapid early growth, and intensive stem elongation. In the present study, the optimal sowing time for the maize variety ‘Pyroxenia’ was late April (A2) and early May (A3). Later sowing of this variety resulted in a reduction in fresh and dry matter yields, as well as a reduction in the quality of the feed. The difference between the first (A1) and the last sowing date (A6) resulted in a 47% reduction in fresh weight and a 49% reduction in dry weight yield. No effect of sowing date was observed on starch content or structural carbohydrates, including crude fiber and its fractions (NDF, ADF, and ADL), in maize forage intended for ensiling. Data analysis for the years 2016–2018 showed that air temperature and precipitation had a significant effect on fresh and dry straw weight yields. Partial factor productivity of nitrogen (PFPF_N) decreased with delayed sowing of maize. On average, this parameter for maize sown in June compared with April, was lower by 38.8% for straw dry yield, 54.5% for ear dry yield, and 46.3% for whole-plant dry yield. Full article

(This article belongs to the Special Issue Synergistic Cultivation of Energy Crops: Maximizing Biomass Yield and Quality via Physiological, Agronomic and Technological Innovations)

33 pages, 4715 KB

Open AccessArticle

Agrivoltaics Can Add Value to High Tunnels in a Subtropical Environment

by Richard Field, Brian Abernathy, Eshwar Ravishankar, Kate Cassity-Duffey and Justin Vaughn

Agronomy 2026, 16(13), 1299; https://doi.org/10.3390/agronomy16131299 - 7 Jul 2026

Abstract

The goal of agrivoltaic engineers is to use growing space for the synergistic production of both food and energy, typically via photovoltaic (PV) capture. Most research in this area has been carried out in arid, high-light environments, but subtropical and temperate regions are [...] Read more.

The goal of agrivoltaic engineers is to use growing space for the synergistic production of both food and energy, typically via photovoltaic (PV) capture. Most research in this area has been carried out in arid, high-light environments, but subtropical and temperate regions are also critical production zones, and installation designs vary considerably. In this study, tomato and lettuce production using an agrivoltaic high tunnel (HT) design specific for a subtropical environment (NE Georgia, USA, USDA Zone 8A) was tested using organic production standards. The design utilized typical HTs (approx. 11 m × 5 m) with solar panel arrays hung internally. The design aimed to (1) meet off-grid power needs, (2) mitigate excessive temperature and humidity, (3) balance shade and plant productivity, and (4) simplify installation and maintenance. Treatments were replicated at the HT level, and cultivar differences were assessed to identify genotypes that might serve in future work to optimize yield under partial shade. In 2023 and 2024, we employed novel organic photovoltaic (OPV) panels, which are partially opaque. The OPV panels provided sufficient energy needs to maintain beneficial conditions without external power sources. In 2024, tomato plants in the OPV HTs experienced an area-weighted daily light integral (DLI, mol photons m⁻² d⁻¹) of approximately 31.8 (95% CI [28.9, 34.7]), compared to 34.7 (95% CI [31.8, 37.6]) in non-OPV HTs, an approximate reduction of 8%. Average maximum temperatures in the OPV HTs were 33.5 °C (95% CI [30.6, 36.4], compared to 35.1 °C (95% CI [30.9, 39.2]) in the non-OPV HTs, an approximate reduction of 1.6 °C. In 2023, tomato marketable yield was reduced by approximately 0.9 kg per plant in OPV HTs compared to non-OPV HTs (p = 0.023). In 2024, yields were statistically equivalent across all treatments (p > 0.1), while marketable fraction was improved relative to 2023 and was greatest in the HTs. Lettuce yield for both years was unaffected by the presence of HTs or OPV panels (p > 0.1). In 2025, we conducted an additional experiment using a shade-equivalent array of conventional 100% opaque photovoltaic (PV) panels and observed a similar reduction in DLI and no significant impact on tomato yield parameters (p > 0.1 Both designs were effective at equilibrating conditions inside the HTs to ambient temperature levels outside the tunnels. Using results from the study, an app for agrivoltaic value estimation was developed. Based on that software, the presented agrivoltaic design under currently available silicon–PV technology achieves an 18% annual return, assuming system depreciation is minimal and surplus energy could be applied to other on-farm needs. Full article

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17 pages, 803 KB

Open AccessArticle

The Effects of Bacterial Consortia Containing Symbiotic Rhizobia on the Seed Germination and Seedling Growth of Several Crop Plants

by Monika Janczarek, Mateusz Grabowski and Maciej Gustab

Agronomy 2026, 16(13), 1298; https://doi.org/10.3390/agronomy16131298 - 7 Jul 2026

Abstract

The growth of vegetable plants is dependent on numerous environmental factors, including the presence of rhizosphere bacteria that produce phytohormones and support mineral uptake. The aim of this study was to determine the effect of symbiotic bacteria belonging to the Rhizobium leguminosarum species [...] Read more.

The growth of vegetable plants is dependent on numerous environmental factors, including the presence of rhizosphere bacteria that produce phytohormones and support mineral uptake. The aim of this study was to determine the effect of symbiotic bacteria belonging to the Rhizobium leguminosarum species and other PGPR bacteria: Bacillus cereus, Chryseobacterium lathyri, and Lysinibacillus fusiformis, on seed germination and plant growth of a few crop species (i.e., white cabbage, broccoli, red pepper, and sugar beet). Three inoculation variants were tested: a mixture of R. leguminosarum strains (R), a mixture of other PGPR bacteria (B), and a combination of both of them (R + B). Biometric parameters such as seed germination, seedling growth, and the length and weight of upper parts and roots were determined. Our results showed diverse responses of the studied crop species to the bacterial mixtures used. In the case of variants R and R + B, the strongest effect of inoculation on seed germination and plant growth was observed. The obtained results indicated the agricultural potential of the analyzed bacterial consortia as biological support for the production of vegetable crops. They also emphasize the need for further research on their effectiveness in various environmental conditions. Full article

(This article belongs to the Special Issue The Rhizobium-Legume Symbiosis in Crops Production)

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16 pages, 2473 KB

Open AccessArticle

A Study on Spectral Inversion Modeling of Biochar Regulation on SPAD Values in Cadmium-Contaminated Maize Leaves

by Si-Yao Gao, Hai-Jun Sun, Qi-Xiang Wang, Jun-Tong Li, Li-Na Zhou, Li-Mei Chen, Chun-Hui Liu, Jian-Lei Qiao, Shuang Liu, Yue Yu and Li-Juan Kong

Agronomy 2026, 16(13), 1297; https://doi.org/10.3390/agronomy16131297 - 6 Jul 2026

Abstract

Cadmium (Cd) contamination in soil poses a serious threat to crop quality. Biochar is widely regarded as an effective amendment that can reduce Cd bioavailability and limit Cd uptake by crops. However, studies on the rapid and nondestructive evaluation of crop physiological responses [...] Read more.

Cadmium (Cd) contamination in soil poses a serious threat to crop quality. Biochar is widely regarded as an effective amendment that can reduce Cd bioavailability and limit Cd uptake by crops. However, studies on the rapid and nondestructive evaluation of crop physiological responses under biochar-mediated alleviation of Cd stress remain insufficient. Spectral modeling methods can enable rapid and nondestructive monitoring of crop physiological status. In this preliminary experiment, Zhengdan 958 maize seedlings grown in Cd-contaminated soil were subjected to five biochar application rates: 0, 10, 30, 50, and 70 g/pot, designated as CK, A1, A3, A5, and A7, respectively. The study established a non-destructive spectral detection model for relative chlorophyll content expressed as SPAD values of maize leaves to achieve spectral inversion of leaf physiological information. The alleviating effect of biochar on Cd stress was evaluated by analyzing SPAD values and Cd accumulation in roots, stems, and leaves. The original spectral data underwent preprocessing steps including multivariate scattering correction, standard normal variable transformation, normalization, trend removal, first-order derivative transformation, and second-order derivative transformation. The effectiveness of different preprocessing methods was compared using partial least squares regression. Feature bands were identified via Pearson correlation analysis, and support vector regression models were established based on genetic algorithm (GA), particle swarm optimization (PSO), and grid search optimization. The results demonstrated that biochar application significantly increased the SPAD values of corn leaves (r = 0.879) and reduced the proportion of bioavailable Cd in soil, with the A7 treatment showing the most substantial decrease (30%). This indicates that biochar effectively mitigates Cd’s inhibitory effect on chlorophyll synthesis, with the alleviation effect enhancing as biochar application rates increased. Validation of the partial least squares regression model revealed that detrended spectra achieved optimal predictive performance (R²c = 0.94, RMSEC = 0.82, R²p = 0.88, RMSEP = 1.15), leading to the development of three optimized support vector regression models: GA-SVR, PSO-SVR, and GS-SVR. The GA-SVR model with a sigmoid kernel demonstrated the best internal validation performance for predicting SPAD values in maize leaves (R²c = 0.95, RMSEC = 0.24; R²p = 0.75, RMSEP = 1.63). This study provides preliminary theoretical support and technical reference for rapid spectral detection of the physiological status of maize under biochar-mediated mitigation of cadmium stress. Full article

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

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20 pages, 2867 KB

Open AccessArticle

Effects of Partial Organic Fertilizer Substitution on Soil Physicochemical Properties, Enzyme Activities, Microbial Communities, and Maize Yield: A Two-Year Field Study

by Chenghang Sun, Xu Yang, Zhonghua Wen and Yuli Lian

Agronomy 2026, 16(13), 1296; https://doi.org/10.3390/agronomy16131296 - 6 Jul 2026

Abstract

Partial substitution of chemical fertilizer with organic fertilizer is an important strategy for optimizing fertilization and mitigating soil degradation caused by excessive chemical fertilizer application. However, systematic studies comparing the effects of different substitution ratios on soil properties, enzyme activities, and microbial communities [...] Read more.

Partial substitution of chemical fertilizer with organic fertilizer is an important strategy for optimizing fertilization and mitigating soil degradation caused by excessive chemical fertilizer application. However, systematic studies comparing the effects of different substitution ratios on soil properties, enzyme activities, and microbial communities remain scarce. A two-year field experiment was conducted with five treatments: no fertilization (Control), chemical fertilizer alone (CF), 20% organic fertilizer substitution (M20), 40% substitution (M40), and 60% substitution (M60). High-throughput sequencing was used to analyze soil bacterial and fungal communities. The M40 treatment significantly increased soil organic matter (17.96% and 30.18%, respectively), available nitrogen (6.85% and 20.30%, respectively), and available phosphorus (30.74% and 52.65%, respectively) compared with CF in both years, with more pronounced improvements observed in 2025. Furthermore, the M40 treatment also enhanced urease and sucrase activities in both years but reduced alkaline phosphatase (ALP) activity in 2025. Microbial community analysis revealed that the M40 treatment enriched beneficial microorganisms, including Proteobacteria, Acidobacteriota, Basidiomycota, Vicinamibacteraceae, Botryotrichum, and Tausonia, while inhibiting the pathogenic fungus Fusarium. Compared with CF, the M40 treatment increased maize yield by 7.04% and 8.10% in 2024 and 2025, respectively, which was the highest among all treatments. Mantel tests indicated that yield was positively correlated with available phosphorus, available potassium, total nitrogen, total phosphorus, and urease activity, but negatively correlated with ALP activity in 2025. Our findings demonstrate that 40% organic fertilizer substitution synergistically improves soil fertility, optimizes microbial community structure, and promotes crop yield, providing empirical evidence for optimizing fertilization regimes in maize production. Full article

(This article belongs to the Special Issue Adaptive Agricultural Strategies: Win–Win Solutions for Climate Change Challenges)

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13 pages, 6867 KB

Open AccessArticle

Phenotypic Screening and Statistical Validation of an Evaluation Scale for Fusarium Dry Rot Resistance in Potato Germplasm

by Carmen Iribar, Leire Barandalla, Amaya Ortiz-Barredo, María de la O. Leyva-Pérez and Jose Ignacio Ruiz de Galarreta

Agronomy 2026, 16(13), 1295; https://doi.org/10.3390/agronomy16131295 - 6 Jul 2026

Abstract

Potato is a globally important staple, but postharvest diseases such as dry rot, caused by Fusarium spp., threaten production and lead to major economic losses and food safety risks. Limited resistant cultivars highlight the need for phenotypic screening and integration with genomic tools [...] Read more.

Potato is a globally important staple, but postharvest diseases such as dry rot, caused by Fusarium spp., threaten production and lead to major economic losses and food safety risks. Limited resistant cultivars highlight the need for phenotypic screening and integration with genomic tools to improve resistance and breeding efficiency. A total of 336 potato genotypes, including 295 commercial varieties and 41 breeding clones, were evaluated under post-harvest conditions following artificial inoculation. Tubers were inoculated with Fusarium sambucinum, and lesion penetration measured to classify susceptibility. Overall, this study provides one of the most comprehensive phenotypic evaluations of dry rot resistance in potato germplasm to date. While no variety was fully resistant, the identification of both moderately susceptible and highly susceptible cultivars offers valuable insights for breeding programs and contributes to the development of more resilient potato production and storage systems. In addition, this phenotypic screening can be integrated with genomic tools to accelerate breeding for improved resistance and postharvest performance. Full article

(This article belongs to the Special Issue Identification and Breeding of High-Quality, Disease-Resistance and High-Producing Varieties)

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18 pages, 3851 KB

Open AccessArticle

Nitrous Oxide Emission Characteristics and Underlying Mechanisms in a Rice–Crab Co-Culture System Under Water and Nitrogen Regulation

by Shengjie Chen, Shiwei Ren, Nan Sun, Songyan Tang, Xuebing Wang, Hao Tian, Yuxi Qiu, Runqi Wang, Xiangyuan Zuo and Kaihan Zhang

Agronomy 2026, 16(13), 1294; https://doi.org/10.3390/agronomy16131294 - 6 Jul 2026

Abstract

Global atmospheric N₂O concentrations have risen to 335 ppb, with agricultural soils serving as a major emission source and rice paddies accounting for approximately 11% of agricultural N₂O emissions. Rice–crab co-culture has been widely adopted because of its potential [...] Read more.

Global atmospheric N₂O concentrations have risen to 335 ppb, with agricultural soils serving as a major emission source and rice paddies accounting for approximately 11% of agricultural N₂O emissions. Rice–crab co-culture has been widely adopted because of its potential to increase and stabilize crop yields; however, the underlying mechanisms of N₂O mitigation and the synergistic effects of crab bioturbation with water and nitrogen management remain unclear. Therefore, in this study, we conducted a two-year field experiment in Zhaodong, Heilongjiang Province, China, to elucidate the N₂O mitigation effects of rice–crab co-culture under water and nitrogen regulation and the associated driving mechanisms. The results showed that rice–crab co-culture significantly reduced N₂O emissions. Specifically, the N₂O flux decreased by 19.9%, while cumulative N₂O emissions decreased by 19.8%. Under the combined regulation of water and nitrogen management, the mitigation effect on N₂O emissions was further enhanced, with a reduction of up to 30.8%. Regarding environmental factors, crab activity combined with shallow wet irrigation reduced soil water content and increased surface temperature. These changes promoted the transformation of nitrogen from inorganic forms to microbially assimilable forms, increasing the microbial nitrogen content by approximately 29.5%. Meanwhile, soil enzyme activities changed significantly: the activities of urease, sucrase, and protease increased, whereas nitrate reductase activity decreased. Structural equation modeling showed that the indirect effect of management practices was much greater than the direct effect, accounting for 63% of the total effect. Nitrogen transformation was the core mitigation pathway, characterized by the conversion of inorganic nitrogen into microbial biomass nitrogen, which reduced substrate availability for nitrification and denitrification. Enzyme activity regulation served as a secondary pathway, mainly through the inhibition of nitrate reductase activity. Overall, the rice–crab system achieved sustained N₂O reduction by improving soil aeration and jointly regulating substrate limitation and weakening nitrogen transformation capacity. Full article

(This article belongs to the Special Issue Agricultural Carbon Sequestration, Emission Reduction, and Efficiency Enhancement: Innovative Practices and Prospects)

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15 pages, 2060 KB

Open AccessArticle

Evaluating Frequency Sampling for Botanical Composition Assessment in Heterogeneous Tropical Grasslands

by Diana Marcela Valencia-Echavarría, Yury Tatiana Granja-Salcedo, Julián Andrés Castillo Vargas, Sorany Milena Barrientos Grajales and Andrea Milena Sierra-Alarcón

Agronomy 2026, 16(13), 1293; https://doi.org/10.3390/agronomy16131293 - 5 Jul 2026

Abstract

Aims: This study aimed to evaluate the agreement of a frequency sampling method (FR) as a tool for species identification while measuring undisturbed sward height. Methods: The botanical composition of both grazing systems was evaluated during the pre-grazing and post-grazing periods [...] Read more.

Aims: This study aimed to evaluate the agreement of a frequency sampling method (FR) as a tool for species identification while measuring undisturbed sward height. Methods: The botanical composition of both grazing systems was evaluated during the pre-grazing and post-grazing periods using two methods: the Dry Weight Rank (DWR) and FR. A non-parametric Friedman test was applied to compare evaluation methods and grazing moments. Differences in detection frequencies between methods were assessed using McNemar’s test for paired binary data. Results: The evaluation method did not influence the relative abundance of the three most abundant plant species identified: U. decumbens, Paspalum genus, and Commelinaceae weeds. A high positive Lin’s concordance correlation coefficient (CCC) was observed between the two methods in U. decumbens, Paspalum genus, U. brizantha cv. Marandú, U. plantaginea, U. arrecta, and U. humidicola (CCC ≥ 0.70). We observed lower agreement for some functional groups, particularly Commelinaceae weeds (CCC = 0.38), narrow-leaf weeds (CCC = 0.46), and Cyperaceae weeds (CCC = 0.17). Canonical correlation analysis (CCA) between the chemical composition of leaves and the botanical composition estimated by the DWR revealed two significant canonical functions (p < 0.01), with canonical correlations of 0.692 and 0.478 for the first and second functions, respectively. When botanical composition estimated by the FR was used as a regressor for leaf chemical composition, three significant canonical functions (p < 0.01) were identified, with canonical correlations of 0.632, 0.529, and 0.425 for the first, second, and third functions, respectively. Conclusions: FR represents a practical and complementary approach for assessing botanical composition and plant diversity in heterogeneous tropical grasslands, particularly for the rapid monitoring of dominant species. However, lower agreement was observed for some low-abundance functional groups, indicating reduced FR sensitivity for certain plant types. Full article

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

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34 pages, 12963 KB

Open AccessArticle

Interpretable Deep Learning for Varroa Mite Detection: Integrating Deblurring, Morphology-Preserving Preprocessing, and Explainability Analysis

by Hong-Gu Lee, Jeong-Yong Shin, Woon-Tak Han, Su-Bae Kim, Min-Jee Kim, Giyoung Kim and Changyeun Mo

Agronomy 2026, 16(13), 1292; https://doi.org/10.3390/agronomy16131292 - 5 Jul 2026

Abstract

Varroa destructor is the most devastating ectoparasite of Apis mellifera, and early detection is critical for colony survival. This study systematically investigated how image preprocessing, model architecture, and feature map resolution jointly affect classification accuracy and Grad-CAM++ explainability in deep-learning-based Varroa detection. [...] Read more.

Varroa destructor is the most devastating ectoparasite of Apis mellifera, and early detection is critical for colony survival. This study systematically investigated how image preprocessing, model architecture, and feature map resolution jointly affect classification accuracy and Grad-CAM++ explainability in deep-learning-based Varroa detection. From comb-surface images of 20 A. mellifera colonies, 3400 region-of-interest images were processed through 12 preprocessing pipelines combining deblurring, histogram normalization, morphology-preserving resizing, and non-morphological resizing. Nineteen CNN architectures, including VarroaNet — a custom lightweight model with configurable channel attention — were screened across all pipelines, and the top six further evaluated at four feature-map resolutions (7 × 7 to 56 × 56); the two stages together comprised 1,548 classification training runs across 516 configurations. Resizing consistently improved classification accuracy, whereas histogram normalization degraded it. VarroaNet (r = 8) achieved the highest mean accuracy across configurations (97.28%) with the lowest cross-configuration variability (CV = 1.47%). The 28 × 28 resolution was jointly optimal for classification and localization at minimal computational overhead, whereas 56 × 56 degraded performance. Notably, classification accuracy and localization quality did not always coincide—the highest-accuracy configuration (ShuffleNet-V2-x1.0 at 14 × 14, 97.34%) achieved an IoU@30 of only 0.160, underscoring the need for explicit localization evaluation. Morphology-preserving resizing achieved higher localization efficiency with zero morphological distortion. The recommended configuration—VarroaNet (r = 8) at 28 × 28 with deblurred MR preprocessing—achieved the highest localization performance (Pointing Game = 0.927), indicating correct attention to the mite region in 92.7% of infested test images. Full article

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

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24 pages, 2262 KB

Open AccessReview

Reframing Weed Detection: From Feature-Based Vision to Crop-Guided Intelligence in Precision Agriculture

by Yanjun Duan, Wenpeng Zhu, Shugui Ding, Mian Li, Kang Han, Xiaoyue Lai, Yuxin Liao, Fuhao Gong, Zhong Li, Maocheng Zhao, Bin Wu and Xiaojun Jin

Agronomy 2026, 16(13), 1291; https://doi.org/10.3390/agronomy16131291 - 5 Jul 2026

Abstract

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy [...] Read more.

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy under controlled and semi-controlled conditions. However, most existing approaches still follow a weed-centric paradigm in which models are trained to explicitly recognize diverse weed species or weed classes. Such strategies face persistent limitations caused by extreme weed morphological variability, crop-weed similarity, high annotation cost, and spatial-temporal heterogeneity across fields, seasons, and cropping systems. This review therefore reframes weed detection as a broader transition from feature-based vision and direct weed recognition toward crop-guided, context-aware, and decision-oriented intelligence. Specifically, we synthesize the literature from three perspectives: (i) methodological evolution, including handcrafted features, machine learning, deep learning, segmentation, and multimodal sensing; (ii) paradigm transformation, from weed-centric detection to crop-guided inference based on crop structure, crop rows, and non-crop vegetation; and (iii) deployment-oriented integration, including edge devices, latency-accuracy-energy trade-offs, and robotic actuation. We further summarize representative public datasets, method categories, crop-guided studies, and edge-platform reporting requirements. Finally, we outline a decision-aware hybrid framework in which crop-guided perception provides low-latency weed localization, while species-level recognition is conditionally activated when required by herbicide selection, resistance management, or high-risk weed control. This synthesis clarifies both the value and the limitations of crop-guided weed detection and outlines actionable directions for scalable, robust, and field-deployable intelligent weeding systems. Full article

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

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16 pages, 3345 KB

Open AccessArticle

Long-Term Fertilizer Postponing Reshapes Spatial and Temporal Patterns of Bacterial Communities and N-Cycling Potential in Paddy Soils

by Yan Zhou, Lei Xu, Junhui Chen and Ganghua Li

Agronomy 2026, 16(13), 1290; https://doi.org/10.3390/agronomy16131290 - 4 Jul 2026

Abstract

Optimizing nitrogen (N) management is essential for sustaining rice productivity and improving soil N retention in paddy ecosystems, yet whether long-term fertilizer postponing (FP) regulates bacterial community assembly and microbial N-cycling potential in a compartment-dependent manner remains unclear. Using soils from an 11-year [...] Read more.

Optimizing nitrogen (N) management is essential for sustaining rice productivity and improving soil N retention in paddy ecosystems, yet whether long-term fertilizer postponing (FP) regulates bacterial community assembly and microbial N-cycling potential in a compartment-dependent manner remains unclear. Using soils from an 11-year field experiment, we investigated bacterial communities and eight N-cycling genes in bulk and rhizosphere soils across three rice growth stages. Compared with conventional fertilization (CF), FP significantly increased grain yield, plant N accumulation, soil NH₄⁺-N (8.1%), microbial biomass N (MBN, 4.3%), and urease activity (30.3%). N-cycling genes showed pronounced temporal variation, generally peaking at the heading stage. FP increased the abundance of genes involved in N fixation, nitrification, and denitrification in bulk soil but reduced most N-cycling genes in the rhizosphere. Although bacterial α-diversity was unchanged, FP significantly altered bacterial community composition. Network and redundancy analysis further showed that bacterial community assembly and N-cycling potential were closely associated with soil C and N status. These findings indicate that long-term FP improves rice productivity by enhancing soil N availability and reshaping bacterial community assembly and microbial N-cycling potential in a compartment-dependent manner, providing new insights into the microbial mechanisms underlying sustainable N management in paddy soils. Full article

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

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21 pages, 1812 KB

Open AccessArticle

Antioxidant Potential of Actinidia arguta Fruit Extracts: A Comparative Study of Cultivar-Dependent Differences

by Małgorzata Olszowy-Tomczyk, Irena Maria Choma, Izabella Świątek, Dominika Siwek and Agnieszka Szopa

Agronomy 2026, 16(13), 1289; https://doi.org/10.3390/agronomy16131289 - 4 Jul 2026

Abstract

Actinidia arguta has recently gained considerable popularity among consumers due to the high nutritional value of its fruits, recognized as a rich source of bioactive compounds, including antioxidants. This study aimed to determine and compare the antioxidant activity (AA) of fruit extracts obtained [...] Read more.

Actinidia arguta has recently gained considerable popularity among consumers due to the high nutritional value of its fruits, recognized as a rich source of bioactive compounds, including antioxidants. This study aimed to determine and compare the antioxidant activity (AA) of fruit extracts obtained from 10 different cultivars. The results of spectrophotometric assays were compared to those of effect-directed dot-blot and thin-layer chromatography (TLC). Total phenolic content (TPC) was determined spectrophotometrically (Folin–Ciocalteu) and verified through high-performance liquid chromatography and TLC. All studied extracts exhibited AA, although significant intracultivar differences were observed. Both spectrophotometric and chromatographic analyses consistently indicated the Vitikiwi and Geneva cultivars as having the highest antioxidant potential among the tested samples. However, chromatographic analyses revealed that the strong AA of the Vitikiwi was not associated with a high polyphenol content, but rather with its exceptionally high level of ascorbic acid, which led to an overestimation of phenolic content in the Folin–Ciocalteu assay. These findings demonstrate that AA in A. arguta fruits may result from different classes of bioactive compounds depending on the cultivar. The study emphasizes the importance of using both spectrophotometric and chromatographic methods to accurately evaluate antioxidant potential in plant-derived food products. Full article

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

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