Agronomy

9 pages, 477 KB

Open AccessEditor’s ChoiceArticle

Assessing Glyphosate Injury and Forage Bermudagrass Regrowth Using Canopeo

by Lucas F. Abreu, Misha R. Manuchehri, João A. Antonangelo, Carla L. Goad and Alexandre C. Rocateli

Agronomy 2026, 16(13), 1272; https://doi.org/10.3390/agronomy16131272 - 30 Jun 2026

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Abstract

Visual injury estimates are criticized for their subjective nature. Thus, a quantitative method might improve glyphosate injury assessment. This study aimed to develop a quantitative method to determine glyphosate injury on two bermudagrass [Cynodon dactylon (L.) Pers.] cultivars, ‘Greenfield’ and ‘Goodwell’, based [...] Read more.

Visual injury estimates are criticized for their subjective nature. Thus, a quantitative method might improve glyphosate injury assessment. This study aimed to develop a quantitative method to determine glyphosate injury on two bermudagrass [Cynodon dactylon (L.) Pers.] cultivars, ‘Greenfield’ and ‘Goodwell’, based on a Canopeo-based green canopy cover reduction (GCCR) method. The experimental design was a completely randomized factorial containing the two bermudagrass cultivars and five glyphosate rates (0.39, 0.53, 1.06, 1.54, and 3.08 kg a.i. ha⁻¹) plus a nontreated control. Visual green canopy cover and GCCR ratings were measured at 8, 16, and 24 days after glyphosate application (DAG). The commonly used visual rating and the Canopeo-based GCCR method correlated. Bland–Altman analysis showed that at low glyphosate rates (0.39 and 0.53 kg a.i. ha⁻¹), the GCCR method overestimated injury compared to visual ratings, while at higher rates (1.54 and 3.08 kg a.i. ha⁻¹), GCCR underestimated injury values by over 30% for Greenfield and 40% for Goodwell. Despite these inconsistencies, both methods yielded similar conclusions. Further research is needed to validate the Canopeo-based GCCR method for other weed species in addition to traditional visual ratings. Full article

(This article belongs to the Section Weed Science and Weed Management)

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17 pages, 1921 KB

Open AccessEditor’s ChoiceArticle

Flight Dynamics of the True Armyworm (Mythimna unipuncta) in a Maize Agroecosystem in Southeast Romania

by Emil Georgescu, Maria Toader, Ioan Sebastian Brumă, Lidia Cană and Horhocea Daniela

Agronomy 2026, 16(13), 1267; https://doi.org/10.3390/agronomy16131267 - 30 Jun 2026

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Abstract

True armyworm (Mythimna unipuncta) is a polyphagous pest that damages forage grasses, small grains, or maize crops. This pest is found in the Americas, Western Africa, Asia, and Europe. The true armyworm was detected in Romania a few decades ago, but [...] Read more.

True armyworm (Mythimna unipuncta) is a polyphagous pest that damages forage grasses, small grains, or maize crops. This pest is found in the Americas, Western Africa, Asia, and Europe. The true armyworm was detected in Romania a few decades ago, but no studies have examined its flight dynamics in crops. This paper presents five years of results from monitoring the flight dynamics of the true armyworm (Mythimna unipuncta) using pheromone traps, and two years of field assessments for larvae scouting at maize plants. The field site is in southeastern Romania, in Călărași County, at the National Agricultural Research and Development Institute in Fundulea, within a temperate continental climate. Five moths were captured in the traps in 2021; 18 moths were captured in 2022; 32 in 2023; 38 in 2024, and 22 moths were captured in 2025. In 2021, the true armyworm flight started on 21 September and ended on 27 October; in 2022, the flight started on 31 October and ended on 25 November; in 2023, the flight started on 23 October and ended on 22 November; in 2024 the flight started on 8 October and ended on 28 November; while in 2025 the flight started on 3 October and ended on 25 November. Results from maize plant assessments for true armyworm larval scouting indicate that no larvae were detected in the last twenty days of July during 2024 and 2025, August, and the first ten days of September. This is the first report in the Romanian literature concerning the constant presence of the true armyworm during autumn in the southeast of this country over the last five years. However, the pest population density in this country did not reach pest levels, but this situation could change in the future due to global warming. Full article

(This article belongs to the Special Issue Ecological Aspects as a Basis for Future Pest Integrated Management—2nd Edition)

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24 pages, 2721 KB

Open AccessEditor’s ChoiceArticle

Cultivar-Specific Expression of the Vintage Effect in Furmint Grapes from the Tokaj Wine Region; Part II: Acid Balance, Potassium Accumulation and Tannin Content

by Csaba Rácz, Krisztina Molnár, Tamás Dövényi-Nagy, Károly Bakó, István Kathy, István Szepsy, László Csige and Attila Csaba Dobos

Agronomy 2026, 16(13), 1253; https://doi.org/10.3390/agronomy16131253 - 29 Jun 2026

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Abstract

Understanding how interannual climatic variability shapes must composition is critical for predicting wine quality under warming conditions, particularly for acid-retaining cultivars such as Vitis vinifera L. cv. Furmint. This study—conducted as a continuation of a previous investigation on Furmint berry weight, total soluble [...] Read more.

Understanding how interannual climatic variability shapes must composition is critical for predicting wine quality under warming conditions, particularly for acid-retaining cultivars such as Vitis vinifera L. cv. Furmint. This study—conducted as a continuation of a previous investigation on Furmint berry weight, total soluble solids and total dry extract—evaluated titratable acidity, pH, potassium, ammonia and tannin content across three contrasting vintages (2022–2024) in the Tokaj wine region. Using a high-resolution meteorological dataset and an extensive climatic parameter matrix, exploratory analysis was conducted to evaluate responses, and the most influential thermal, radiation-related and water-balance related climatic factors associated with each must parameter were identified. Total acidity and pH showed consistent sensitivity to climatic variability: acidity decreased with mid-season warm nights and abundant summer rainfall, while pH was inversely associated with extreme heat events but increased under higher early-season rainfall and post-véraison irradiation. Potassium content exhibited partly atypical responses, showing positive correlations with late-season warm nights and frequent summer precipitation, and negative with early heat. Ammonia displayed weak to moderate climatic dependence, while tannic acid consistently decreased with higher thermal and irradiation loads. Overall, these results imply cultivar-specific climatic responses in Furmint and suggest that temperature extremes, nighttime heat and rainfall timing are important factors shaping must composition, providing a foundation to better understand the expression of vintage effects under climate change. Full article

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

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34 pages, 3402 KB

Open AccessEditor’s ChoiceArticle

Aromatic Plant Residues from Essential Oil Steam Distillation as a Potential Source of Antioxidants

by Giuseppe Squillaci, Virginia Carbone, Carmen Di Pipi, Francesco La Cara and Alessandra Morana

Agronomy 2026, 16(13), 1240; https://doi.org/10.3390/agronomy16131240 - 26 Jun 2026

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Abstract

Steam distillation residues (SDRs) of aromatic plants were utilized to produce antioxidant extracts using hydroalcoholic solvents with increasing percentages of ethanol (0, 50, 75 and 100% v/v). The phenolic composition and antioxidant power were measured and compared to the corresponding [...] Read more.

Steam distillation residues (SDRs) of aromatic plants were utilized to produce antioxidant extracts using hydroalcoholic solvents with increasing percentages of ethanol (0, 50, 75 and 100% v/v). The phenolic composition and antioxidant power were measured and compared to the corresponding fresh aromatic plants (FAPs). The largest amount of polyphenols, ranging from 14.15 (lemon balm FAP) and 19.61 (lavender SDR) mg gallic acid equivalents/g dry matter (DM), was found in 0% ethanol (pure water) extracts. The phenolic content of lavender and spearmint SDR extracts was higher than that of the corresponding FAP extracts, while the opposite was observed with lemon balm. Rosmarinic acid was the most abundant hydroxycinnamic acid detected, ranging from 608.65 µg/g DM in lemon balm 50% ethanol FAP extract to 697.47 µg/g DM in spearmint 50% ethanol SDR extract. The lavender and spearmint SDR extracts exhibited higher antioxidant power than the FAP extracts, while the extracts from fresh lemon balm were more antioxidant than the SDR. The lavender 50% ethanol SDR extract showed the highest scavenging activity (67.16%) and ferric reducing power (16.60 mg ascorbic acid equivalents/g DM). These results prove that spent aromatic residues can be utilized to produce antioxidant blends for various industrial applications. Full article

(This article belongs to the Special Issue Agricultural Biomass Waste Conversion into Value-Added Products—2nd Edition)

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16 pages, 3172 KB

Open AccessEditor’s ChoiceArticle

Effects of Thermal Sanitization on Insect Frass Nutrient Composition and Biofertilizer Performance in a Lettuce Pot Trial

by Julietta Moustaka, Hanne Lakkenborg Kristensen and Mesfin Tsegaye Gebremikael

Agronomy 2026, 16(13), 1242; https://doi.org/10.3390/agronomy16131242 - 26 Jun 2026

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Abstract

Insect farming has rapidly expanded in Europe following regulatory approval of insect-derived proteins in aquaculture feed and increasing interest in the valorization of insect by-products. Insect frass, consisting of excreta and exuviae, is a nutrient-rich material with beneficial microorganisms and potential as a [...] Read more.

Insect farming has rapidly expanded in Europe following regulatory approval of insect-derived proteins in aquaculture feed and increasing interest in the valorization of insect by-products. Insect frass, consisting of excreta and exuviae, is a nutrient-rich material with beneficial microorganisms and potential as a sustainable alternative to conventional fertilizers, although its composition varies with insect species and feedstock. EU legislation requires thermal sanitization prior to market release, yet the effects of the thermal treatment on frass nutrient composition and biofertilizer performance remain poorly understood. Insect frass from black soldier flies (BSFFs) fed on a diet based on dairy industry byproducts was sanitized and mixed with sandy soil and used in two lettuce pot trials under greenhouse conditions. The aim of our study was to determine the effects of thermal sanitization on (1) macro- and micronutrient contents and dynamics (plant N and P uptake); and (2) biofertilizer potential, including plant physiology (chlorophyll, anthocyanins, flavonols, Fv/Fm), plant growth (biomass), and soil microbial activity (dehydrogenase and β-glucosaminidase). BSFF showed a clear potential to induce growth of lettuce plants by increasing chlorophyll content, biomass and microbial activity. Furthermore, the sanitization process did not significantly alter the measured agronomic performance of frass under the tested conditions or reduce its benefits on biomass growth, chlorophyll content, microbial enzyme activity and on nutrient uptake by the lettuce plants. These findings suggest that the mandatory sanitization does not compromise its agronomic functionality, supporting its strong potential within circular agricultural systems under the tested conditions. However, the results are valid under greenhouse conditions and for the specific frass, soil and crop combinations; field validation is needed to confirm these results under large-scale high-value crop production conditions. Full article

(This article belongs to the Special Issue Plant Nutrition Eco-Physiology and Nutrient Management)

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33 pages, 35069 KB

Open AccessEditor’s ChoiceArticle

Evolution of Climate–Agriculture Research from 1990 to 2025: A Large-Scale Bibliometric and Semantic Mapping Analysis

by Estrella Alcalá-Espinosa and Adolfo Peña-Acevedo

Agronomy 2026, 16(13), 1223; https://doi.org/10.3390/agronomy16131223 - 24 Jun 2026

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Abstract

Climate change is reshaping agricultural systems by altering temperature and rainfall regimes, increasing the frequency of extreme events, and intensifying risks to crop productivity, water use, and farm decision-making. As climate–agriculture research expands rapidly, it becomes increasingly difficult to identify consolidated knowledge domains, [...] Read more.

Climate change is reshaping agricultural systems by altering temperature and rainfall regimes, increasing the frequency of extreme events, and intensifying risks to crop productivity, water use, and farm decision-making. As climate–agriculture research expands rapidly, it becomes increasingly difficult to identify consolidated knowledge domains, emerging priorities, and evidence gaps. This study maps the structure and evolution of this literature using 219,261 Scopus-indexed documents selected from 290,560 records published between 1990 and 2025. A text-mining workflow combined BERTopic-based semantic modeling with supervised thematic classification into 18 macro-themes, while annual shares, z-scores, and document-level primary–secondary co-framing were used to assess temporal salience and cross-theme coupling. The results show sustained growth in research output, with 53.67% of publications produced between 2016 and 2025, and strong geographical concentration in the United States and China, which together account for 41.98% of the corpus. Hydrology and water management, crop production, impact assessment, and atmospheric processes remain central pillars, while socio-economic vulnerability, food security, sustainability, biotechnology, and greenhouse gas mitigation have gained prominence. The resulting evidence map provides a reproducible overview of the climate–agriculture knowledge landscape and can support research prioritization and policy design for climate-resilient agrifood systems. Full article

(This article belongs to the Collection A Series of Special Reviews and Topic Analyses That Explore Major Trends and Challenges in Agronomy)

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16 pages, 2372 KB

Open AccessEditor’s ChoiceArticle

Selenium Biofortification Improves Grain Quality and Reduces Arsenic Accumulation in Rice Under Alternate Wetting and Drying Irrigation

by María J. Poblaciones, Luis Vicente, Damián Fernández-Rodríguez, Ángel Albarrán, David Peña and Antonio López-Piñeiro

Agronomy 2026, 16(13), 1220; https://doi.org/10.3390/agronomy16131220 - 24 Jun 2026

Viewed by 364

Abstract

Rice production is under increasing threat from adverse climatic trends that exacerbate water scarcity and compromise food safety. The need to transition toward water-saving irrigation is urgent, as is the requirement of addressing the dual burden of selenium (Se) deficiency and arsenic (As) [...] Read more.

Rice production is under increasing threat from adverse climatic trends that exacerbate water scarcity and compromise food safety. The need to transition toward water-saving irrigation is urgent, as is the requirement of addressing the dual burden of selenium (Se) deficiency and arsenic (As) toxicity. This 3-year field study (2020–2022) is the first to evaluate the effects of integrated water-saving irrigation. Permanent flood irrigation (Flood) or alternate wetting and drying was used, in which fields were reflooded when the soil matric potential reached −20 kPa (Reflood-20) and −70 kPa (Reflood-70); the effects of foliar Se biofortification at 15 g Se ha⁻¹ with sodium selenate (15-Se) or no Se (No-Se) on rice production and Se and As accumulation were also investigated. The results identified the Reflood-20 regime as the optimal strategy, achieving 36% water savings without significant grain yield penalties while enhancing grain quality. Foliar Se application successfully increased the dehulled grain Se content by 10.7-fold, effectively meeting human dietary requirements. The As contents were decreased by 27.6% due to water restriction, and an additional 10% loss was observed because of Se supplementation. Analysis of the straw also showed a 23.5% decrease in As and a 5.7-fold increase in Se. Consequently, the synergy between moderate deficit irrigation and Se biofortification provides a robust, cost-effective framework for the large-scale production of safer, nutrient-dense rice, reconciling resource efficiency with food security. Full article

(This article belongs to the Special Issue Agroecological Strategies for Improving Nutritional Quality of Cereal Crops)

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19 pages, 27354 KB

Open AccessEditor’s ChoiceArticle

Sustainable Weed Management and Mass Trapping Strategies in Mediterranean Organic Citrus Orchards Under Semi-Arid Conditions, Andarax Valley (Spain)

by Juan Torres, María Ángeles Moreno-Teruel, Patricia Marín-Membrive, Araceli Peña-Fernández and Diego Luis Valera-Martínez

Agronomy 2026, 16(12), 1209; https://doi.org/10.3390/agronomy16121209 - 22 Jun 2026

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Abstract

Organic citrus production in semi-arid Mediterranean regions is increasingly challenged by water scarcity, soil degradation, and rising phytosanitary pressure associated with climate change. This study evaluated different sustainable management strategies under commercial organic citrus production conditions in the Andarax Valley (Almería, southeastern Spain). [...] Read more.

Organic citrus production in semi-arid Mediterranean regions is increasingly challenged by water scarcity, soil degradation, and rising phytosanitary pressure associated with climate change. This study evaluated different sustainable management strategies under commercial organic citrus production conditions in the Andarax Valley (Almería, southeastern Spain). Two complementary field trials were conducted: (i) the assessment of four weed management systems—shallow tillage, mechanical mowing, sown cover crop, and partial manual mowing—and (ii) the comparison of four mass-trapping systems for the control of Ceratitis capitata. Fruit quality parameters, yield performance, and trapping efficacy were evaluated under commercial organic farming conditions. Weed management treatments did not significantly affect internal fruit quality parameters, including juice content, total soluble solids, titratable acidity, and maturity index, which were mainly determined by cultivar-related factors. In contrast, yield showed significant responses to treatment, growing season, and cultivar. The sown cover crop treatment (T3) produced the highest mean yields in both growing seasons, reaching 56.6 and 72.9 kg tree⁻¹ in seasons 1 and 2, respectively. In the mass-trapping trial, the liquid trap baited with hydrolyzed protein (R-9) showed the highest capture efficacy (0.060 flies trap⁻¹ day⁻¹), significantly outperforming the control treatment (0.014 flies trap⁻¹ day⁻¹) and the other evaluated trapping systems. Conversely, dry trap models (A-9 and V-8) recorded significantly lower capture rates (FTD < 0.01), which may be associated with lower retention efficiencies documented in the literature for dry-killing designs. All treatments exhibited high female selectivity (>94%). In addition, a pronounced edge effect was detected, with significantly higher captures concentrated along the orchard perimeter. Overall, the results support the integration of functional cover crops and perimeter mass-trapping strategies as sustainable tools to improve resilience and pest management in Mediterranean organic citrus production systems. Full article

(This article belongs to the Special Issue Pests, Pesticides, Pollinators and Sustainable Farming—2nd Edition)

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23 pages, 3022 KB

Open AccessEditor’s ChoiceArticle

In-Field Assessment of Olive Fruit Quality Using a Low-Cost Multispectral Sensor and ANN Models

by Miguel Noguera, Borja Millán, Arturo Aquino and José Manuel Andújar

Agronomy 2026, 16(12), 1198; https://doi.org/10.3390/agronomy16121198 - 19 Jun 2026

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Abstract

Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive [...] Read more.

Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive for smallholder farmers. To address this, we propose a methodology using a custom-made, low-cost multispectral device. Built upon the AS7265x board, the system acquires 18 spectral bands in the visible and near-infrared range (410–940 nm). We used these spectral data to feed artificial neural network (ANN) models for estimating the quality of intact olives. During a two-season field experiment, we monitored ripening to acquire spectral signatures and ground-truth values for oil content per fresh weight (OCFW), oil content per dry matter (OCDM), moisture (M), and titratable acidity (TA). External validation showed high accuracy for OCFW (R²p = 0.86), OCDM (R²p = 0.86), and M (R²p = 0.89), proving the system’s reliability. However, TA estimation showed lower performance (R²p = 0.21), indicating limited spectral correlation. These findings pave the way for affordable, real-time smart farming tools for olive quality monitoring. Full article

(This article belongs to the Special Issue Spectroscopy in Modern Agriculture: Current Achievements, Challenges, and Future Perspectives)

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15 pages, 1045 KB

Open AccessEditor’s ChoiceArticle

Olive Yield Prediction in the Mediterranean Basin: Bibliometric Evidence of Precision Agricultural Engineering Gaps and Innovation Priorities for Sustainable Agri-Food Systems

by Francesco Toscano, Paola D’Antonio, Lucas Santos Santana and Costanza Fiorentino

Agronomy 2026, 16(12), 1189; https://doi.org/10.3390/agronomy16121189 - 18 Jun 2026

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Abstract

This bibliometric study maps olive (Olea europaea L.) yield prediction research as a coherent scientific domain for the first time. A Scopus query (27 February 2026) yielded 84 peer-reviewed articles (2002–2025), from which co-authorship network analysis, Bradford’s and Lotka’s Laws, Latent Dirichlet [...] Read more.

This bibliometric study maps olive (Olea europaea L.) yield prediction research as a coherent scientific domain for the first time. A Scopus query (27 February 2026) yielded 84 peer-reviewed articles (2002–2025), from which co-authorship network analysis, Bradford’s and Lotka’s Laws, Latent Dirichlet Allocation topic modelling (LDA), and OLS regression on citation counts were applied. Publication output increased nearly fourfold across three periods: 1.7 articles yr⁻¹ (2002–2014), 4.4 yr⁻¹ (2015–2019), and 6.7 yr⁻¹ (2020–2025). The 84 articles involve 382 authors, 61 journals, and 1551 citations (H-index = 22). Network analysis reveals a concentrated Spanish–Italian co-authorship axis. OLS regression (adj. R² = 0.267) identifies article age and abstract length as the only significant citation predictors, consistent with cumulative exposure time and study scope as structural drivers. Term-frequency screening against 18 a priori concepts finds that transfer learning, federated learning, hyperspectral imaging, digital twins, and SHAP-based explainability are absent or marginal. The field is producing more papers than ever on a narrowing methodological base geographically concentrated in the Mediterranean basin. Priority gaps—explainable AI, multi-region datasets, sensor-fusion pipelines, and federated data infrastructure—align directly with European Farm to Fork and Horizon Europe objectives. Full article

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

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11 pages, 427 KB

Open AccessEditor’s ChoiceArticle

Host Suitability of Winter Cover Crops for Meloidogyne enterolobii

by Churamani Khanal, Sagar GC, Homan Regmi and David Harshman

Agronomy 2026, 16(12), 1171; https://doi.org/10.3390/agronomy16121171 - 16 Jun 2026

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Abstract

The guava root-knot nematode (Meloidogyne enterolobii) is a highly aggressive species of root-knot nematode that is not manageable with currently existing nematode management methods. This study was conducted to evaluate the suppressive ability of winter cover crops against M. enterolobii. [...] Read more.

The guava root-knot nematode (Meloidogyne enterolobii) is a highly aggressive species of root-knot nematode that is not manageable with currently existing nematode management methods. This study was conducted to evaluate the suppressive ability of winter cover crops against M. enterolobii. Eleven winter cover crops (rye, wheat, barley, triticale, oat, Austrian winter pea, crimson clover, balansa clover, hairy vetch, purple top turnip, and daikon radish) were evaluated against M. enterolobii in a growth room environment. Root-knot nematode-susceptible tomato (Solanum lycopersicum cv. Rutgers) was used as a control. Nematode reproduction on cover crops ranged from 1 to 501,373 eggs/g root, with oat supporting the least nematode reproduction and crimson clover supporting the greatest nematode reproduction. The crops significantly suppressing egg production on roots and second-stage juveniles in the soil relative to the control were oat, winter pea, wheat, barley, rye, and triticale. Hairy vetch, purple turnip, daikon radish, and crimson clover were good hosts, while balansa clover, wheat, winter pea, barley, rye, triticale and oat were poor or non-hosts, with the latter four crops producing substantial biomasses. Employment of these cover crops that suppress or do not support M. enterolobii reproduction while adding substantial biomass to the soil may lead to sustainable nematode management. Full article

(This article belongs to the Special Issue Soil Microbial and Nematode Communities: Diversity, Function, and Responses to Agronomic Practices)

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19 pages, 2139 KB

Open AccessEditor’s ChoiceArticle

Opuntia ficus-indica Mucilage Coating as a Potential Natural Strategy to Preserve Lemon Quality During Cold Storage

by Francesco Gargano, Giuseppe Greco, Federica Torregrossa, Raimondo Gaglio, Luca Settanni, Paolo Inglese and Giorgia Liguori

Agronomy 2026, 16(12), 1173; https://doi.org/10.3390/agronomy16121173 - 16 Jun 2026

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Abstract

The main causes of lemon fruit senescence and deterioration are fungal diseases and postharvest quality loss. Edible coatings have been proposed to delay quality loss in fresh produce by reducing moisture loss and helping preserve external appearance. Natural functional coatings are increasingly being [...] Read more.

The main causes of lemon fruit senescence and deterioration are fungal diseases and postharvest quality loss. Edible coatings have been proposed to delay quality loss in fresh produce by reducing moisture loss and helping preserve external appearance. Natural functional coatings are increasingly being investigated as potential alternatives to synthetic waxes and preservatives due to environmental and consumer safety concerns. The effect of a natural edible coating based on Opuntia ficus-indica mucilage on extending the shelf-life of lemons during cold storage was investigated. Lemon fruits were treated with the mucilage-based edible coating and subsequently stored under controlled cold conditions. Coated and uncoated lemon fruits were evaluated for their physicochemical properties, including weight loss, total soluble solids, pH, titratable acidity, color, and microbiological analysis, as well as total polyphenol content and antioxidant activity, over a 60-day storage period at 5 ± 0.5 °C and 95% relative humidity. The results showed that the mucilage-based coating improved lemon fruit storage performance, effectively preserving key physicochemical and microbiological parameters over 60 days of cold storage (p ≤ 0.05). In particular, the treatment maintained fruit firmness, reduced weight loss (up to 45%), increased juice content (up to 1.8-fold), and delayed microbial decay compared to control samples. Coated fruits also exhibited higher total polyphenolic content and antioxidant activity than control samples at the end of storage. In addition, using mucilage extracted from cactus pear cladode waste provides a sustainable way to add value to the product, with promising industrial applications as an alternative to synthetic fruit coatings. Full article

(This article belongs to the Special Issue Innovative Technology and Postharvest Management Strategies to Improve the Quality and Shelf-Life of Horticultural Products)

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20 pages, 2755 KB

Open AccessEditor’s ChoiceArticle

Respiration Dynamics and Thermal Sensitivity (Q₁₀) in Rainfed Crops in Mediterranean Soils Under Different Tillage and Fertilization Systems

by José Antonio Mediano-Guisado, Paula Madejón, Elena Fernández-Boy, Engracia Madejón and María T. Domínguez

Agronomy 2026, 16(12), 1174; https://doi.org/10.3390/agronomy16121174 - 16 Jun 2026

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Abstract

Mediterranean agricultural systems are highly vulnerable to increased climatic variability, which threatens soil water availability and the functionality of the soil carbon (C) cycle. Soil management practices strongly influence water dynamics and C-substrate quality, thus potentially affecting the temperature sensitivity of soil respiration. [...] Read more.

Mediterranean agricultural systems are highly vulnerable to increased climatic variability, which threatens soil water availability and the functionality of the soil carbon (C) cycle. Soil management practices strongly influence water dynamics and C-substrate quality, thus potentially affecting the temperature sensitivity of soil respiration. We evaluated the combined effects of tillage (traditional tillage, TT; reduced tillage, RT), fertilization (mineral, MF; addition of biosolid compost, BC), and rainfall inputs (ambient conditions, C; reduction of 30% rainfall inputs, EX) on soil water content (SWC) and storage (SWS), and in situ soil respiration (Resp) dynamics over three agricultural seasons in a Mediterranean legume–wheat rotation, using a factorial field experiment. We also evaluated how the sensitivity of soil respiration to temperature could be affected by tillage and fertilization types in a complementary laboratory experiment under controlled moisture and temperature conditions. RT was effective in improving SWS and mitigating surface desiccation, although this advantage was attenuated in wet years due to homogenization of moisture along the soil profile. Soil Resp was primarily controlled by SWC. BC stimulated soil respiration mainly during the first crop season, with a residual non-significant trend in the third season. This effect appeared constrained under dry periods, although no significant fertilization × rainfall exclusion interaction was detected. The diurnal cycle of Resp showed a clear decoupling from diurnal soil temperature. Crucially, the intrinsic thermal sensitivity of respiration (Q₁₀) remained stable across all tillage and fertilization treatments, suggesting that field variability is driven by water dynamics and crop phenology and not by microbial responses to changes in substrate availability. Our results confirmed the hierarchical role of climate on C-cycling processes. Full article

(This article belongs to the Section Farming Sustainability)

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25 pages, 6628 KB

Open AccessEditor’s ChoiceArticle

Reverse Agroclimatology: Growing Degree Days at Actual Olive Grove and Vineyard Locations Across Europe

by Ioannis Charalampopoulos, Nikolaos Kotsidis and Fotoula Droulia

Agronomy 2026, 16(12), 1162; https://doi.org/10.3390/agronomy16121162 - 13 Jun 2026

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Abstract

Climate change is progressively altering the thermal environment of European agriculture, with direct consequences for high-value perennial crops such as olive (Olea europaea L.) and grapevine (Vitis vinifera L.). Although the Growing Degree Days (GDD) index is widely applied to characterize [...] Read more.

Climate change is progressively altering the thermal environment of European agriculture, with direct consequences for high-value perennial crops such as olive (Olea europaea L.) and grapevine (Vitis vinifera L.). Although the Growing Degree Days (GDD) index is widely applied to characterize crop thermal requirements, no systematic evidence exists on the actual GDD values accumulated at the locations where these crops are currently grown across Europe. This study introduces a “reverse agroclimatology” approach that anchors GDD calculations exclusively to olive grove and vineyard areas identified in the Corine Land Cover (CLC) dataset for five reference years (1990, 2000, 2006, 2012, and 2018), using ERA5-Land reanalysis daily temperature data as the climatological input. For each CLC reference year, GDD was computed for olive cultivation (T_base = 7 °C, January–May) and viticulture (T_base = 10 °C, April–October) exclusively over registered cultivation pixels, and per-country means were subjected to linear regression trend analysis (p < 0.05). For olive cultivation across 11 Mediterranean countries, statistically significant positive GDD trends were detected in 7 countries, with long-term (1985–2023) country means ranging from 476.2 GDD in France to 1214.3 in Cyprus, indicating that we can revise the known GDD thresholds. The first appearance of olive cultivation in Slovenia’s 2012 CLC dataset, with a median of 546.5 GDD, provides land use-mapped evidence of a spatial displacement of cultivation boundaries. For vineyard cultivation across 22 European countries, significant positive trends were identified in 18 countries, with warming rates reaching 19.25 GDD yr⁻¹ in Turkey, 15.83 GDD yr⁻¹ in Albania, and 14.89 GDD yr⁻¹ in Bosnia and Herzegovina. Mediterranean and Balkan vineyards already exceed the classical 2000 GDD threshold of viticultural suitability across all reference years. In contrast, central and northern European registered vineyards operate below it, though their warmest sites are increasingly approaching or crossing it in the most recent periods. The cultivation-anchored GDD framework, built on openly available data and a fully reproducible R-based pipeline, provides a practical and updatable tool for monitoring the evolving thermal conditions of European olive and wine production under ongoing climate change. Full article

(This article belongs to the Special Issue Sustainable Management of Vineyards and Olive Orchards Under Climate Change)

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19 pages, 1603 KB

Open AccessEditor’s ChoiceArticle

Soybean Monoculture Is Associated with Suppression of Foliar Sudden Death Syndrome Expression Without Consistent Reductions in Pathogen Levels in Ontario Agroecosystems

by Razan Malla, Kari E. Dunfield, Lori A. Phillips, Ashley E. Wragg, Derek J. Lawrence and Owen S. Wally

Agronomy 2026, 16(12), 1160; https://doi.org/10.3390/agronomy16121160 - 13 Jun 2026

Viewed by 499

Abstract

Sudden death syndrome (SDS) and soybean cyst nematode (SCN) are major yield-limiting diseases in North American soybean production, with limited effective management options. Long-term soybean monoculture has been reported to suppress SDS and SCN, but the mechanisms, onset, and persistence of such suppression [...] Read more.

Sudden death syndrome (SDS) and soybean cyst nematode (SCN) are major yield-limiting diseases in North American soybean production, with limited effective management options. Long-term soybean monoculture has been reported to suppress SDS and SCN, but the mechanisms, onset, and persistence of such suppression remain poorly understood. To study these mechanisms, a six-year field study (2018–2023) was conducted at two Ontario sites with contrasting disease histories: Chatham (conducive) and Essex (suppressive). We evaluated suppression development and resilience across soybean monoculture (SSSSSS) and corn–soybean rotations (SCSCSC/CSCSCS), using eight cultivars differing in SDS and SCN resistance across two maturity groups. In Chatham, disease index (DX) progressively declined under monoculture; the most susceptible cultivar, HS11RY07, declined from a mean DX of 89 to 43 by year six, with corresponding yield increases, and rotational yield advantages diminished. In Essex, introducing corn rotation increased SDS symptoms during soybean phases; monoculture yields became comparable to rotation in later years. Importantly, suppression developed without corresponding reductions in Fusarium virguliforme and SCN populations, which remained variable across years, suggesting that monoculture may disrupt pathogen effectiveness rather than eliminating it. This decoupling of pathogen abundance and disease severity is consistent with soil-mediated biological suppression; the microbial drivers are addressed in subsequent work. Full article

(This article belongs to the Section Pest and Disease Management)

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36 pages, 4531 KB

Open AccessEditor’s ChoiceSystematic Review

Trends in Global Soil Research and a Microbiome-Based Framework for Soil Health Assessment

by Njomza Gashi, Maja Mikolás, Péter Dávid, Péter Fauszt, Ferenc Gál, László Stündl, Judit Remenyik and Melinda Paholcsek

Agronomy 2026, 16(12), 1154; https://doi.org/10.3390/agronomy16121154 - 12 Jun 2026

Viewed by 1300

Abstract

Soil health is fundamental for food security, climate regulation, and ecosystem resilience, yet global research and development efforts remain uneven and fragmented. To provide a more integrated perspective, this study examines global soil health research and development trends between 1990 and 2025 while [...] Read more.

Soil health is fundamental for food security, climate regulation, and ecosystem resilience, yet global research and development efforts remain uneven and fragmented. To provide a more integrated perspective, this study examines global soil health research and development trends between 1990 and 2025 while proposing a microbiome-based framework for soil health assessment. Using a PRISMA-based methodology, we combined data from international development projects, scientific publications, patent data, and microbiome-related initiatives to evaluate temporal, thematic, and regional patterns in soil research. The results reveal a sustained increase in global soil-related research, with nutrient management and soil degradation remaining dominant topics, while soil microbiome research and carbon sequestration have emerged as the fastest-growing areas, particularly since 2015. However, significant regional disparities persist, with research concentrated in Asia, Europe, and North America. To address the lack of a coherent microbiome-based soil health assessment system, we propose a structured microbial indicator framework based on twelve functional microbial groups, evaluated through culturable abundance, functional gene abundance, and relative abundance. Additionally, we introduce a unified, database-driven microbiome reference framework that interprets soils relative to known types and conditions. Overall, this study highlights the global transition toward biologically driven and system-oriented soil research and provides a conceptual foundation for more standardized, scalable, and ecologically meaningful soil health assessment. Full article

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

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25 pages, 1020 KB

Open AccessEditor’s ChoiceArticle

Economic and Environmental Assessment of Handling and Packaging Phase of Fresh Lemons in Southeastern Spain

by Begoña García Castellanos, José García García, Benjamín García García and Caridad Rosique Jiménez

Agronomy 2026, 16(12), 1135; https://doi.org/10.3390/agronomy16121135 - 10 Jun 2026

Viewed by 367

Abstract

This study establishes a warehouse model specific to the handling and packaging of fresh lemons in southeast Spain (Fino and Verna, both conventional and organic) into two types of packaging (mesh and corrugated box) and carries out an economic and environmental evaluation using [...] Read more.

This study establishes a warehouse model specific to the handling and packaging of fresh lemons in southeast Spain (Fino and Verna, both conventional and organic) into two types of packaging (mesh and corrugated box) and carries out an economic and environmental evaluation using Life Cycle Costing (LCC) and Life Cycle Assessment (LCA). The boundary of the assessment is the warehouse gate, not considering the following phases (distribution and consumption). The warehouse model is based on data from on-site surveys in four medium-sized companies representative of the studied area. The results of this production phase are analyzed, as well as the aggregates of the whole production chain, from cultivation to the dispatching of packaged products at the gate of the warehouse. Production costs between 0.450 €·kg⁻¹ and 0.545 €·kg⁻¹, with organic options being generally more expensive, although it is the packaging that accounts for the biggest differences. The cost in the aggregate production chain shows a wider range, from 0.73 €·kg⁻¹ to 0.99 €·kg⁻¹. In terms of employment, the production chain generates 0.58 agricultural work unit (AWU)·ha⁻¹. The environmental results of the production chain show that the warehouse phase (handling and packaging) has a significant environmental impact. The handling stage shows little variation between lemon varieties or types of management, in contrast to the cultivation phase, where significant differences are observed. In comparative terms, in the production chain: conventional management has a greater environmental impact than organic management in most categories. The Verna variety has a greater impact than Fino and corrugated box packaging is systematically more impactful than mesh. Fresh lemons from southeast Spain have a low global warming impact (0.096–0.152 kg CO₂ eq·kg lemon⁻¹) compared to the literature, mainly due to the low impact during the cultivation phase. Full article

(This article belongs to the Section Farming Sustainability)

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20 pages, 1653 KB

Open AccessEditor’s ChoiceArticle

Design and Greenhouse Sensing-Layer Validation of a Low-Cost Modular Agricultural Robot for Environmental Sensing, Telemetry and Remote Supervision in Precision Agriculture

by Bálint Ambrus, Gergely Teschner, Attila József Kovács, Miklós Neményi, Norbert Boros and Anikó Nyéki

Agronomy 2026, 16(12), 1139; https://doi.org/10.3390/agronomy16121139 - 10 Jun 2026

Viewed by 449

Abstract

Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node [...] Read more.

Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 × 25 × 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland–Altman limits of agreement. The pressure channel showed strong trend tracking (R² = 0.992, RMSE = 0.024 hPa), whereas air temperature (R² = 0.756, RMSE = 2.537 °C) and relative humidity (R² = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment. Full article

(This article belongs to the Special Issue Advancing Plant Phenotyping for Precision Crop Growth Monitoring and Forecast Leveraging In Situ, Remote and Proximal Sensing, and AI)

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20 pages, 4515 KB

Open AccessEditor’s ChoiceArticle

Short-Term Repeatability of Multispectral UAV Measurements and Implications for Vegetation Index Stability

by Mikael Änäkkälä, Pirjo S. A. Mäkelä and Antti Lajunen

Agronomy 2026, 16(12), 1134; https://doi.org/10.3390/agronomy16121134 - 10 Jun 2026

Viewed by 345

Abstract

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have become valuable tools in precision agriculture, enabling the monitoring of crop health, biomass estimation, and stress detection. However, the effectiveness of these measurements depends on several factors, including repeatability, sensitivity, and accuracy. Understanding these [...] Read more.

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have become valuable tools in precision agriculture, enabling the monitoring of crop health, biomass estimation, and stress detection. However, the effectiveness of these measurements depends on several factors, including repeatability, sensitivity, and accuracy. Understanding these factors is crucial to ensure reliable data collection, particularly in regions with fluctuating weather patterns. This study evaluated the sensitivity of multispectral data collected within a short time frame and its impact on vegetation indices in normal field conditions. Measurements were taken over three days, with three UAV flights performed each day. Multispectral data were analyzed to identify statistically significant differences in vegetation indices, with calculations performed independently for each measurement day. The repeatability of vegetation indices varied between measurement days. When all measurement days were analyzed together, GARI, GNDVI, NDRE, and NDVI were the only indices that did not show statistically significant differences between flights. However, the magnitude of differences varied depending on the index, with some indices showing only minor variations between flights. Full article

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

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15 pages, 1289 KB

Open AccessEditor’s ChoiceArticle

A Preparation Containing Rhizobial Lipochitooligosaccharides Improves Pea Productivity in the Field

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

Agronomy 2026, 16(12), 1133; https://doi.org/10.3390/agronomy16121133 - 10 Jun 2026

Viewed by 328

Abstract

The effect of biopreparations containing rhizobial nod factors (lipochitooligosaccharides, LCOs) on pea growth and yield was tested under field conditions. Experiments were conducted using Pisum sativum cv. Batuta as a model, which was grown on two different soils in very similar weather conditions. [...] Read more.

The effect of biopreparations containing rhizobial nod factors (lipochitooligosaccharides, LCOs) on pea growth and yield was tested under field conditions. Experiments were conducted using Pisum sativum cv. Batuta as a model, which was grown on two different soils in very similar weather conditions. Rhizobial metabolites were applied at three different concentrations and the effect of the treatment was studied at flowering and at full maturity of plants. At both sites an increase in the root nodule number and mass, acceleration of the plant growth rate, and increase in the mass of roots and aboveground parts of plants after the application of preparations containing LCOs were observed. Despite adverse climatic conditions (low rainfall from flowering to maturity), the application of preparations with LCOs resulted in a significant increase in the pea yield, ranging from 11 to 16%, which supports the use of such preparations in pea field cultivation. Full article

(This article belongs to the Section Farming Sustainability)

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33 pages, 14936 KB

Open AccessEditor’s ChoiceArticle

Genome-Wide Dissection of Early and Late Leaf Spot Resistance in Advanced Peanut Backcross Lines Carrying Introgressions from Arachis stenosperma and Arachis batizocoi

by Namrata Maharjan, Mounirou H. Alyr, David J. Bertioli and Soraya C. M. Leal-Bertioli

Agronomy 2026, 16(12), 1129; https://doi.org/10.3390/agronomy16121129 - 9 Jun 2026

Viewed by 453

Abstract

Early and late leaf spot (ELS and LLS), caused by Passalora arachidicola and Nothopassalora personata, are major constraints to peanut (Arachis hypogaea L.) production. Durable resistance in cultivated germplasm remains limited due to the crop’s narrow genetic base. Wild Arachis species [...] Read more.

Early and late leaf spot (ELS and LLS), caused by Passalora arachidicola and Nothopassalora personata, are major constraints to peanut (Arachis hypogaea L.) production. Durable resistance in cultivated germplasm remains limited due to the crop’s narrow genetic base. Wild Arachis species represent an important but underutilized source of resistance. This study aimed to identify and prioritize wild introgressions associated with foliar disease resistance in advanced peanut backcross lines derived from the induced allotetraploid BatSten1 (Arachis batizocoi × A. stenosperma)^4x. A population of advanced backcross lines carrying reduced wild genome content (~5% to ~1% across advancement) was evaluated through four years of field trials for LLS severity and yield, complemented by detached-leaf bioassays to dissect resistance components for both ELS and LLS. Genome-wide SNP genotyping, combined with mixed-model analysis and association mapping, identified introgressed regions influencing disease response. Genome-wide association studies (GWAS) detected loci on chromosomes A06 and A09 associated with LLS resistance, explaining approximately 25% and 11% of phenotypic variation, respectively, with evidence of additive effects between loci. Component-level analyses further revealed both resistance- and susceptibility-associated introgressions. Although tomato spotted wilt virus (TSWV) incidence was evaluated in field trials, exploratory GWAS did not detect significant marker–trait associations, indicating that genetic components associated with this trait were not resolved under the conditions tested. Overall, these results expand the understanding of the genetic architecture of leaf spot resistance beyond traditional donor sources and provide a framework for prioritizing beneficial wild introgressions while minimizing linkage drag in peanut pre-breeding programs. Full article

(This article belongs to the Special Issue Shaping the Future of Plant Breeding: Harnessing Phenomic and Genomic Insights for Disease Resistance)

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16 pages, 2345 KB

Open AccessEditor’s ChoiceArticle

Effects of Mineral Filler Composition on Pellet Properties, Seed Quality, and Seedling Establishment of Pelleted Chinese Cabbage Seeds

by Mac Cheryl Sulan Charles Emparang, Sang-Rim Kim, Faraaz Ahmed Mohammad, Ji-Gu Lee, Min-Geon Cho, Min-Jae Kim, Dae-Geun Jeong, Kyung-Min Park and Jum-Soon Kang

Agronomy 2026, 16(11), 1119; https://doi.org/10.3390/agronomy16111119 - 5 Jun 2026

Viewed by 410

Abstract

Seed pelleting improves seed handling and precision sowing, but its performance depends strongly on the physicochemical properties of filler materials. This study evaluated the effects of talc-based mineral filler combinations on pellet characteristics, germination, greenhouse emergence, seed vigor, and seedling growth of Chinese [...] Read more.

Seed pelleting improves seed handling and precision sowing, but its performance depends strongly on the physicochemical properties of filler materials. This study evaluated the effects of talc-based mineral filler combinations on pellet characteristics, germination, greenhouse emergence, seed vigor, and seedling growth of Chinese cabbage (Brassica rapa L. var. pekinensis). Talc (TC) was used alone or combined with bentonite (BE), calcium carbonate (CC), and diatomaceous earth (DE). Pellet physical properties, morphology, and surface elemental composition were analyzed using hardness measurements, porosity analysis, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. TC + BE exhibited excessive swelling-driven water retention, prolonged disintegration time, and severe surface cracking, which were associated with reduced germination, delayed emergence, and poor seed vigor. In contrast, TC + CC + DE showed balanced physicochemical properties, including adequate hardness, moderate porosity, acceptable disintegration time, and improved water-holding capacity, producing superior greenhouse emergence while maintaining seedling growth comparable to the unpelleted control. Overall, successful seed pelleting depended on balancing structural integrity, water retention, and mass transfer properties within the pellet matrix. TC + CC + DE appears to be a promising formulation for Chinese cabbage seed pelleting. Full article

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

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22 pages, 5046 KB

Open AccessEditor’s ChoiceArticle

Grain Sorghum as a Climate-Resilient Alternative to Maize: Evapotranspiration, Water-Use Efficiency, and Yield Under Weed Competition and Reproductive-Stage Drought

by Ariel Tóth, Zoltán Tóth, Kristóf Kozma-Bognár and Brigitta Simon-Gáspár

Agronomy 2026, 16(11), 1110; https://doi.org/10.3390/agronomy16111110 - 4 Jun 2026

Cited by 1 | Viewed by 628

Abstract

Climate change is expected to increase the frequency and severity of drought events in Europe, necessitating the identification of more water-efficient cropping systems. This study compared the evapotranspiration dynamics, water-use efficiency, and yield performance of maize (Zea mays L.) and grain sorghum [...] Read more.

Climate change is expected to increase the frequency and severity of drought events in Europe, necessitating the identification of more water-efficient cropping systems. This study compared the evapotranspiration dynamics, water-use efficiency, and yield performance of maize (Zea mays L.) and grain sorghum (Sorghum bicolor L. Moench) under controlled field conditions using a Thornthwaite–Mather-type compensation evapotranspirometer. Three water regimes (100%, 50%, and 30% of optimal water supply) were applied during the reproductive stage, combined with weed-free and weed-infested treatments. Under moderate water deficit (50% water supply), grain sorghum maintained stable grain yield, while maize grain yield decreased by 17.98%. Under severe water deficit (30% water supply), grain yield reductions reached 36.04% in maize and 42.80% in sorghum. Grain sorghum consistently required less water and used 2.87–38.17% less water to produce 1 kg of grain compared to maize across treatments. Weed interference was associated with a lower yield and water-use efficiency in both species, while severe water deficit (70%) caused substantial declines in all measured parameters. Evapotranspiration was primarily driven by solar radiation and temperature, with reduced sensitivity under increasing water limitation. Overall, the results suggest that grain sorghum may represent a viable alternative to maize under moderate drought conditions; however, both crops require supplemental irrigation under severe water scarcity. The study highlights the importance of integrated weed management and provides novel insights into crop water-use dynamics under combined abiotic and biotic stress conditions. Full article

(This article belongs to the Special Issue Optimizing Crop Water Use: Advances and Applications in Deficit Irrigation Strategies—2nd Edition)

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20 pages, 401 KB

Open AccessEditor’s ChoiceArticle

Bio-Insecticidal Potential of Salvia spp. Against Tuta absoluta

by Poonam Devi, Emanuele Rosa, Anna Paola Lanteri, Andrea Minuto, Valentina Parisi, Mauro Giacomini, Norbert Maggi and Angela Bisio

Agronomy 2026, 16(11), 1113; https://doi.org/10.3390/agronomy16111113 - 4 Jun 2026

Viewed by 356

Abstract

The tomato leaf miner (Tuta absoluta) is recognized as a highly destructive pest affecting members of the Solanaceae family, particularly tomato crops, where infestations may cause total crop loss. Its rapid spread and increasing resistance to chemical insecticides underscore the urgent [...] Read more.

The tomato leaf miner (Tuta absoluta) is recognized as a highly destructive pest affecting members of the Solanaceae family, particularly tomato crops, where infestations may cause total crop loss. Its rapid spread and increasing resistance to chemical insecticides underscore the urgent need for innovative, environmentally compatible control strategies. In this context, the present study investigates the bioactivity of surface extracts derived from four Salvia species (S. buchananii, S. corrugata, S. discolor, and S. namaensis) against T. absoluta larvae, focusing on their insecticidal and feeding-deterrent effects. Chemical characterization through LC–MS analysis demonstrated that these Salvia species contain diverse secondary metabolites, including diterpenoids, triterpenoids, and flavonoids. Initial screening using a leaf-dip bioassay at a concentration of 2.50 mg/mL showed that S. discolor was particularly effective among the Salvia extracts tested. Subsequent dose–response assays with S. discolor extracts (0.16–5.00 mg/mL) confirmed strong larvicidal and feeding inhibitory effects, with LC₅₀ and FI₅₀ values of 0.12 and 0.13 mg/mL, respectively. Additionally, weak inhibition of acetylcholinesterase (AChE) was observed, suggesting a minor contribution of neurotoxic effects to the overall activity of the extract. The findings suggest that S. discolor extracts may be useful for managing T. absoluta infestations, pending evaluation of their effects on non-target organisms. Full article

(This article belongs to the Section Pest and Disease Management)

21 pages, 21631 KB

Open AccessEditor’s ChoiceArticle

YOLO-CornSeg: A Lightweight Segmentation Model for Corn Seedlings with an Indirect Weed Detection Strategy

by Jinglin Lei, Jialin Yu, Kang Han, Mian Li, Xiaojun Jin and Honglian Yin

Agronomy 2026, 16(11), 1091; https://doi.org/10.3390/agronomy16111091 - 31 May 2026

Viewed by 527

Abstract

Weed control is crucial for optimizing corn yield. In recent years, advances in computer vision and deep learning have created new opportunities for precision agriculture. However, annotating weed datasets is typically time-consuming, labor-intensive, and costly. To address this challenge, this study proposes an [...] Read more.

Weed control is crucial for optimizing corn yield. In recent years, advances in computer vision and deep learning have created new opportunities for precision agriculture. However, annotating weed datasets is typically time-consuming, labor-intensive, and costly. To address this challenge, this study proposes an indirect weed detection strategy that reduces reliance on explicit weed annotations by focusing on accurate crop segmentation. Specifically, we develop YOLO-CornSeg, a lightweight segmentation model based on an improved YOLOv8n architecture, designed for precise corn seedling segmentation. The model incorporates a C2f_DWR module to enhance multi-scale feature extraction and a Segment_Efficient head to improve segmentation performance while maintaining computational efficiency. Based on the resulting segmentation masks, an indirect weed detection strategy is applied, in which non-crop green regions are identified as weeds using HSV-based image processing. Experimental results show that YOLO-CornSeg achieves a mean Intersection over Union (mIoU) of 91.1% with a model size of 8.3 MB, outperforming several state-of-the-art two-stage semantic segmentation models while maintaining low computational complexity and a compact model size. The improved segmentation accuracy further enhances the reliability of downstream weed inference. Overall, this study highlights the potential of combining lightweight crop segmentation with indirect weed detection strategies to support precision herbicide application. Full article

(This article belongs to the Special Issue Integrated Weed Management for Field Crops: Innovations, Integration, and Impact)

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15 pages, 1437 KB

Open AccessEditor’s ChoiceReview

Landscape-Level Integrated Pest Management Strategies for Stink Bugs in Soybean–Maize Agroecosystems of the Neotropics

by Weidson Plauter Sutil, Antônio Ricardo Panizzi and Adeney de Freitas Bueno

Agronomy 2026, 16(11), 1087; https://doi.org/10.3390/agronomy16111087 - 31 May 2026

Viewed by 1062

Abstract

The crop system of soybean–maize succession has been adopted widely in the Neotropics. It inadvertently provides continuous food resources (green bridges) to stink bugs (Hemiptera: Pentatomidae), favoring outbreaks. Thus, stink bugs need to be managed within a broader and more holistic perspective. Not [...] Read more.

The crop system of soybean–maize succession has been adopted widely in the Neotropics. It inadvertently provides continuous food resources (green bridges) to stink bugs (Hemiptera: Pentatomidae), favoring outbreaks. Thus, stink bugs need to be managed within a broader and more holistic perspective. Not just individual fields but the whole landscape should be monitored and managed, since these pest outbreaks are deeply influenced by neighboring fields and successive crops in the same field. During the first crop season, stink bugs should be controlled only in the reproductive stage of soybean (from the R3 to R6 plant development stage), when the population is equal to or higher than the economic threshold (ET) of two stink bugs·m⁻¹. Biological control or plant resistance strategies should be used instead of chemicals whenever possible. When the ET is reached at R7 or R8, more tolerant maize varieties (fast growing) should be sown in the second crop season with the seed treatment using recommended insecticides. Grain losses during harvest and the presence of weeds must be avoided at the end of the soybean season. Chemical insecticide sprayings on maize might still be necessary if Diceraeus spp. outbreaks equal or surpass three stink bugs·m⁻¹ during early maize stages (until V7). This more precise and less impactful management of the agroecosystem will promote a more sustainable and resilient management of these polyphagous pests. Full article

(This article belongs to the Special Issue Strategies for Effective Integrated Pest Management in Staple Crop Production)

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24 pages, 4057 KB

Open AccessEditor’s ChoiceArticle

Intelligent Classification of Soybean Threshing Mixtures Based on Edge Perception and CNN–Transformer Hybrid Architecture

by Shiguo Wang, Caiyuan Zhang, Xiaohu Guo, Chenlong Fan and Xuegeng Chen

Agronomy 2026, 16(11), 1086; https://doi.org/10.3390/agronomy16111086 - 31 May 2026

Viewed by 533

Abstract

To address the low monitoring accuracy of traditional methods caused by the complex composition and severe signal overlapping of threshed materials during soybean threshing, this study proposes a high-precision impact signal classification system based on edge perception and a hybrid deep learning architecture, [...] Read more.

To address the low monitoring accuracy of traditional methods caused by the complex composition and severe signal overlapping of threshed materials during soybean threshing, this study proposes a high-precision impact signal classification system based on edge perception and a hybrid deep learning architecture, serving as a foundational step for threshing loss monitoring. At the hardware level, a high-speed parallel sensing system was developed to achieve continuous acquisition and high-fidelity mapping of transient impact signals. At the algorithmic level, a CNN–Transformer hybrid network was constructed to effectively extract local signal features and capture long-term temporal dependencies, successfully decoupling complex collision dynamics. Bench tests demonstrate that the hybrid model achieves a comprehensive classification accuracy of 97.36% and F1-scores above 0.96 for soybean grains, stems, and pods, significantly outperforming single networks. Furthermore, feature visualization confirms that the model effectively extracted features strongly correlated with the intrinsic impact dynamics of different materials rather than simply fitting environmental noise. This study provides a highly robust algorithm foundation and bench-level engineering reference for the intelligent classification of soybean harvesting mixtures, laying the groundwork for actual loss estimation under real field conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Human–Computer Interactions in Agricultural Production)

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17 pages, 2948 KB

Open AccessEditor’s ChoiceArticle

Seed Priming with PEG Improves the Growth, Photosynthesis, and Recovery Capacity of SUB1DRO1 and DRO1 Near-Isogenic Lines Under Drought

by Alex Tamu, Aquilino Lado Legge Wani, Sheik Hassan Gbla and Jui-Ichi Sakagami

Agronomy 2026, 16(11), 1066; https://doi.org/10.3390/agronomy16111066 - 28 May 2026

Viewed by 434

Abstract

This study evaluated the effects of polyethylene glycol concentrations in enhancing the physiological performance of the rice varieties and their recovery ability after drought stress. The experiment comprised of IR64, NIL-SUB1DRO1, and NIL-DRO1. Seed priming was conducted by submerging [...] Read more.

This study evaluated the effects of polyethylene glycol concentrations in enhancing the physiological performance of the rice varieties and their recovery ability after drought stress. The experiment comprised of IR64, NIL-SUB1DRO1, and NIL-DRO1. Seed priming was conducted by submerging 5 g of samples in petri dishes containing 100 mL of 5% and 10% PEG solutions. Drought stress significantly reduced all the growth traits, with the susceptible genotypes IR64 recorded highest reduction of shoot length 36%, tiller number 41.3%, shoot dry weight 77%, and root dry weight 72% compared to non-primed NIL-DRO1 and NIL-SUB1DRO1 with reduction in shoot length 34–35%, tiller number 34–45%, root dry weight 60–66%, and shoot dry weight (70–71%). Similar results were recorded for IR64, Pn, 63%, gs 78% E 66%, and RWC 66%, respectively, compared with NIL-DRO1 (55%, 60%, and 58%), while NIL-SUB1DRO1 showed reductions of 55%, 50%, and 54%. PEG 5% and 10% significantly enhanced primed IR64 Pn (29–57%), gs (70%), E (56–64%), and RWC 65%. During the recovery phase, primed seedlings showed a more rapid restoration of growth and photosynthetic efficiency than the non-primed seedlings. PEG 5% and 10% were effective in mitigating drought stress and enhanced recovery ability of rice. Full article

(This article belongs to the Collection Abiotic Stress Tolerance in Plants: Towards a Sustainable Agriculture)

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56 pages, 3538 KB

Open AccessEditor’s ChoiceReview

A Review of Non-Thermal Plasma Technology and Plasma–Artificial Intelligence Integration in Agriculture

by Liangtong Yao and Jianmin Gao

Agronomy 2026, 16(11), 1067; https://doi.org/10.3390/agronomy16111067 - 28 May 2026

Viewed by 579

Abstract

As agriculture moves towards green transformation and low-carbon production, the high energy consumption, environmental burden, and residue risks associated with conventional chemical fertilisers, pesticides, and disinfectants have become increasingly prominent. Non-thermal plasma (NTP) can generate reactive oxygen and nitrogen species (RONS) under near-ambient [...] Read more.

As agriculture moves towards green transformation and low-carbon production, the high energy consumption, environmental burden, and residue risks associated with conventional chemical fertilisers, pesticides, and disinfectants have become increasingly prominent. Non-thermal plasma (NTP) can generate reactive oxygen and nitrogen species (RONS) under near-ambient temperature and pressure conditions, while offering low chemical residue, high reactivity, and modular equipment design. It has therefore attracted growing attention in agricultural engineering and green agricultural input preparation. This review focuses primarily on studies published within the past five years, together with the selected foundational literature retrieved from Web of Science, Scopus, PubMed, MDPI, and ScienceDirect. It systematically examines the fundamental mechanisms, application modes, and representative agricultural scenarios of NTP, with particular emphasis on agricultural nitrogen fixation and fertilisation, seed treatment and seedling raising, crop growth regulation and protection, soil improvement and remediation, and postharvest preservation and safety treatment of agricultural products. Key technological advances are then summarised, including optimisation of discharge systems and reactor configurations, plasma–catalysis synergy, preparation of plasma-activated water (PAW) and plasma-activated mist (PAM), and the development and integration of specialised agricultural equipment. In addition, the current state-of-the-art (SOA) of artificial intelligence (AI) applications in plasma-process modelling, process-parameter optimisation, agricultural performance evaluation, and intelligent control is discussed. Existing evidence indicates that NTP is particularly relevant to controlled-environment agriculture, including greenhouse cultivation, hydroponics, and aeroponics, where discharge processes, water or nutrient solutions, and crop root-zone management can be coupled for in situ nitrogen supply, activated-medium preparation, and crop protection. However, reported effects remain strongly dependent on discharge type, energy input, reactive-species composition, treatment dose, crop species, cultivation system, and application route. Therefore, NTP-based agricultural technologies should be evaluated using consistent indicators, including energy consumption, product selectivity, reactive-species stability, treatment throughput, crop response, ecological safety, and system-level integration with AI and IoT. Future research should prioritise high-efficiency reactors, standardised evaluation frameworks, cross-scale mechanistic understanding, reliable datasets, and closed-loop intelligent control, thereby supporting the transition from laboratory studies to reproducible and application-oriented agricultural systems. Full article

(This article belongs to the Special Issue High-Voltage Plasma Applications in Agriculture)

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15 pages, 676 KB

Open AccessEditor’s ChoiceArticle

Preharvest Biostimulant–Calcium Application Enhances Blueberry Fruit Quality Through Structural and Cuticular Modifications

by Tiago Lopes, Ana Paula Silva, Helena Ferreira, Carlos Ribeiro, Fábio Pereira, António A. Vicente and Berta Gonçalves

Agronomy 2026, 16(11), 1063; https://doi.org/10.3390/agronomy16111063 - 27 May 2026

Viewed by 650

Abstract

The increased demand for higher-quality, longer-lasting blueberries has led to the development of preharvest strategies to improve their structural integrity sustainably. This study analysed the effects of the foliar application of two biostimulant–calcium (Ca) combinations, using Ecklonia maxima extract (EM + Ca) and [...] Read more.

The increased demand for higher-quality, longer-lasting blueberries has led to the development of preharvest strategies to improve their structural integrity sustainably. This study analysed the effects of the foliar application of two biostimulant–calcium (Ca) combinations, using Ecklonia maxima extract (EM + Ca) and glycine betaine (GB + Ca), on yield, biometric, mechanical, and histological properties, as well as cuticular wax composition of blueberries. Both biostimulants increased yield per plant and fruit weight and size in ‘Duke’, with superior results for GB + Ca. Fruit yield increased by 80% with GB + Ca and 40% with EM + Ca. Histological analysis showed increases in cuticle thickness, epidermal cell area and thickness, and hypodermal cell area and area/perimeter ratio. This thicker, denser tissue ultimately improved blueberries’ mechanical properties. Specifically, ‘Draper’ berries treated with GB + Ca had 36%, 15%, and 20% higher values for flesh firmness, stiffness, and deformation work, respectively, relative to the control. However, cuticular wax accumulation was more pronounced with EM + Ca for the ‘Duke’ cultivar, increasing by 12%. Overall, GB + Ca had the greatest impact on blueberry structural quality and may represent a promising strategy to improve postharvest quality and commercial production. Full article

(This article belongs to the Special Issue Biostimulant-Mediated Crop Stress Tolerance: From Physiological Mechanisms to Yield Improvement)

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17 pages, 1873 KB

Open AccessEditor’s ChoiceArticle

Earthworm Community Metrics and Soil Attributes Are Driven by the Addition of Cattle Horn Shavings Fertilizer

by Anna Mazur-Pączka, Kevin R. Butt, Marcin Jaromin, Edmund Hajduk, Mariola Garczyńska, Joanna Kostecka and Grzegorz Pączka

Agronomy 2026, 16(11), 1043; https://doi.org/10.3390/agronomy16111043 - 25 May 2026

Viewed by 414

Abstract

One of the fundamental recommendations for sustainable agricultural practices is protecting soil biodiversity by increasing the use of organic fertilizers and substrates. According to EU regulations, certain animal by-products (including horn shavings) may be used as crop fertilizers; however, insufficient information is available [...] Read more.

One of the fundamental recommendations for sustainable agricultural practices is protecting soil biodiversity by increasing the use of organic fertilizers and substrates. According to EU regulations, certain animal by-products (including horn shavings) may be used as crop fertilizers; however, insufficient information is available on the impact of this fertilizer substrate on the soil environment. This study was conducted to determine the effects of annual soil application of horn shavings on selected characteristics of Lumbricidae communities and physicochemical properties of the soil. Experimental plots had the following treatments of cattle horn shavings (CHS): CHS100 (100%; 1.3 t·ha⁻¹; equivalent to 161 kg N/ha), CHS75 (75%; 0.98 t·ha⁻¹), CHS50 (50%; 0.65 t·ha⁻¹), and SL (control without fertilization). After 2 years of application, an electrical method was used to collect earthworms over the following 3 years. Earthworms found belonged to five species representing three ecological groups: Dendrobaena octaedra, Dendrodrilus rubidus tenuis, Lumbricus rubellus, Aporrectodea caliginosa, and Lumbricus terrestris. Significantly higher values of earthworm metrics were demonstrated between the plot with the highest fertilization (CHS100) and the plots with lower horn shavings additions (abundance: CHS100 > CHS75 and CHS50 by a mean of 43.2%; biomass: CHS100 > CHS75 and CHS50 by a mean of 43%). Species richness was not affected but an increase in CHS application led to a greater biodiversity index. CHS treatments affected selected soil parameters to varying degrees, with soil moisture having the greatest influence on the given earthworm traits. Cattle horn shavings used as a fertilizer are a positive promoter of earthworms in soils and further research in this area may be warranted. Full article

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

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38 pages, 3906 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Research and Applications of Intelligent Manipulators in Agriculture

by Weijie Wu and Jianmin Gao

Agronomy 2026, 16(11), 1041; https://doi.org/10.3390/agronomy16111041 - 24 May 2026

Viewed by 686

Abstract

Agricultural intelligent manipulators are essential for autonomous operations in smart agriculture. However, their industrial deployment faces critical bottlenecks, including perception failures, crop damage, and poor cost–benefit ratios in unstructured environments. Following the PRISMA guidelines, this study reviewed 22 key representative studies and 78 [...] Read more.

Agricultural intelligent manipulators are essential for autonomous operations in smart agriculture. However, their industrial deployment faces critical bottlenecks, including perception failures, crop damage, and poor cost–benefit ratios in unstructured environments. Following the PRISMA guidelines, this study reviewed 22 key representative studies and 78 related studies (2015–2026). This review analyzes mechanisms for low-damage and high-precision operations across hardware (rigid–flexible structures), perception (multi-modal fusion), and decision-making (intelligent control). We compare operational efficiency and damage rates in harvesting, transplanting, and sorting, finding that rigid–flexible actuators with vision-guided force control are key to overcoming current limitations. To evaluate these technologies, we established a benchmarking framework across fruit/vegetable harvesting, seedling grafting, and precision plant protection to assess four technological trajectories. We also address engineering challenges: machinery–agronomy misalignment, high sensor costs, and limited edge computing. Notably, we introduce an economic payback period analysis to evaluate commercial feasibility. Ultimately, future research should prioritize lightweight variable-stiffness hardware, synchronous visuo-tactile perception, and digital twins to seamlessly integrate machinery and agronomy. Full article

(This article belongs to the Special Issue Research Progress in Agricultural Robots in Arable Farming)

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57 pages, 9973 KB

Open AccessEditor’s ChoiceReview

Digital Twin- and AI-Enabled Intelligent Optimisation Design of Agricultural Machinery: A Review

by Pengsheng Ding and Jianmin Gao

Agronomy 2026, 16(11), 1038; https://doi.org/10.3390/agronomy16111038 - 24 May 2026

Viewed by 1309

Abstract

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain [...] Read more.

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

(This article belongs to the Special Issue Digital Twin and AI-Enhanced Simulation in Agricultural Systems)

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26 pages, 2296 KB

Open AccessEditor’s ChoiceArticle

Rapid Decomposition of Brittle Rice Straw Reduces Greenhouse Gas Emissions and Shifts Carbon Allocation in Paddy Soils

by Jerickson Manuel Dela Cruz, Cheng-Hsien Lin, Shan-Li Wang, Chang-Sheng Wang, Yu-Ting Liu, Kuo-Chen Yeh and Yu-Yu Kung

Agronomy 2026, 16(11), 1035; https://doi.org/10.3390/agronomy16111035 - 23 May 2026

Viewed by 430

Abstract

Rice (Oryza sativa L.) straw-return can improve soil carbon (C) sequestration, but its adoption in intensive rice systems is limited by short fallow periods (<30 days), which likely lead to incomplete straw decomposition and increase methane emissions under continuous flooding (CF). Brittle [...] Read more.

Rice (Oryza sativa L.) straw-return can improve soil carbon (C) sequestration, but its adoption in intensive rice systems is limited by short fallow periods (<30 days), which likely lead to incomplete straw decomposition and increase methane emissions under continuous flooding (CF). Brittle rice straw, characterized by lower recalcitrant fiber content and rapid decomposition, may overcome this constraint; however, its environmental performance under alternate wetting and drying (AWD) remains unclear, such as broader C allocation. This 150-day microcosm study evaluated the interaction of straw type (brittle vs. non-brittle) and water management (CF vs. AWD) on greenhouse gas (GHG) emissions, dissolved C production, soil C storage, and aggregate formation in two contrasting paddy soils (sandy loam vs. silty clay loam). Compared with non-brittle straw, brittle straw returns reduced net GHG emissions by approximately 28.4% under CF and 39.6% under AWD. The combination of brittle straw with AWD produced the lowest net GHG emissions (0.61 kg CO₂-eq m⁻²), indicating that intermittent oxygen input effectively mitigated the early decomposition-related emission risk. Brittle straw also increased the concentrations of dissolved inorganic C by 14.2% and nitrate by 64.3% under AWD, suggesting enhanced mineralization and potential inorganic C stabilization. Regardless of straw type, straw return improved soil C stocks by 27.3% in sandy loam and 29.6% in silty clay loam, while also promoting macroaggregate formation. Overall, this study demonstrated that coupling brittle rice straw with AWD can reduce GHG emissions while maintaining soil C benefits, offering a promising residue management strategy for intensive rice cultivation. 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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14 pages, 848 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Nutrient Tracking Tool (NTT) in Predicting Corn Yield Under Various Management Practices

by Kennedi Harris and Ali Saleh

Agronomy 2026, 16(10), 1021; https://doi.org/10.3390/agronomy16101021 - 21 May 2026

Viewed by 498

Abstract

The Nutrient Tracking Tool (NTT) is a free and user-friendly modeling program developed by the Texas Institute for Applied Environmental Research (TIAER) at Tarleton State University in cooperation with the USDA Office of Environmental Markets. NTT simulates various cropping systems to evaluate management [...] Read more.

The Nutrient Tracking Tool (NTT) is a free and user-friendly modeling program developed by the Texas Institute for Applied Environmental Research (TIAER) at Tarleton State University in cooperation with the USDA Office of Environmental Markets. NTT simulates various cropping systems to evaluate management practices that optimize crop production while improving water quality and quantity. The objective of this study is to evaluate the capability of NTT to predict corn yield under different agricultural management scenarios. To assess model performance, 45 management scenarios from three field studies conducted in Iowa, Colorado, and Kansas were replicated in NTT. These scenarios included variations in nutrient sources and application rates, tillage practices, seeding rates, and irrigation management. Field data, including location, slope, planting dates, tillage practices, fertilization rates, and soil properties, were entered into NTT, and simulated crop yields were compared with measured values reported in the studies. Results showed strong agreement between measured and predicted corn yields across the evaluated scenarios. For example, the average measured yield of combined strip-tillage and manure treatment reported by Al-Kaisi and Kwaw-Mensah was 9.48 Mg ha, while NTT predicted 9.45 Mg ha. Similarly, for Halvorson et al., NTT predicted a yield of 8.06 Mg ha, compared with the measured yield of 8.23 Mg ha. Overall, the results indicate that NTT can reliably predict corn yield under a range of management practices, demonstrating its potential as a decision-support tool for agricultural management. Full article

(This article belongs to the Special Issue Modeling for Risk Assessment of Crop Health and Yield Prediction)

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24 pages, 1260 KB

Open AccessEditor’s ChoiceArticle

Predicting Greenhouse Gas Emissions in Agriculture: Production Dynamics, Labor Productivity, and Implications for Climate-Neutral Farming Systems

by Anca Antoaneta Vărzaru

Agronomy 2026, 16(10), 1020; https://doi.org/10.3390/agronomy16101020 - 21 May 2026

Viewed by 511

Abstract

This study explicitly assesses how crop and livestock production, along with real labor productivity, affect greenhouse gas emissions in agriculture across the European Union (EU), considering both per capita and total emissions. Using annual Eurostat data for EU Member States from 2008 to [...] Read more.

This study explicitly assesses how crop and livestock production, along with real labor productivity, affect greenhouse gas emissions in agriculture across the European Union (EU), considering both per capita and total emissions. Using annual Eurostat data for EU Member States from 2008 to 2024, the research applies multiple regression models and a multivariate General Linear Model (GLM) to evaluate structural relationships, complemented by Holt exponential smoothing and ARIMA models to analyze temporal dynamics and generate forecasts. The empirical results indicate that crop and livestock production have a statistically significant positive effect on emissions, while real labor productivity has a significant negative impact. The models explain over 92% of the variation in total emissions and over 95% of the variation in per capita emissions, confirming strong explanatory power. Forecasts show continued growth in agricultural output but a declining trend in per capita emissions, primarily driven by productivity improvements. These findings demonstrate that improvements in labor efficiency and technological progress can partially offset the environmental pressures associated with increased agricultural production. The study concludes that achieving climate-neutral agriculture in the EU is feasible through sustained productivity gains and innovation-driven transformation. Full article

(This article belongs to the Special Issue Enhancing Crop Productivity and Mitigating Environmental Footprints in Sustainable Agriculture)

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18 pages, 2226 KB

Open AccessEditor’s ChoiceArticle

Organic Lentil Production in Switzerland: Evaluation of Genotypes for Agronomical, Qualitative, and Sensory Traits

by Anna Blatter, Katrin Rehak, Despoina Sidiropoulou, Jonas Inderbitzin and Jürg Hiltbrunner

Agronomy 2026, 16(10), 1013; https://doi.org/10.3390/agronomy16101013 - 21 May 2026

Viewed by 363

Abstract

Lentils constitute a strategically important crop within sustainable agricultural systems, particularly in the context of rising global demand for plant-based protein sources. In Switzerland, approximately 95% of lentil seeds are imported, underscoring the untapped potential for domestic production. This study systematically evaluated the [...] Read more.

Lentils constitute a strategically important crop within sustainable agricultural systems, particularly in the context of rising global demand for plant-based protein sources. In Switzerland, approximately 95% of lentil seeds are imported, underscoring the untapped potential for domestic production. This study systematically evaluated the performance of multiple lentil genotypes, alongside optimal seeding densities and growing seasons, through a series of field experiments conducted over five years. In addition, a sensory evaluation was performed on 12 selected genotypes to assess consumer-relevant quality traits. The findings revealed substantial variability in yield among genotypes, ranging from 0.9 to 1.6 t/ha; however, interannual variation exerted a more pronounced influence, with yields fluctuating between 0.1 and 2.0 t/ha. Notably, autumn-sown lentils achieved yields of up to 2.7 t/ha in three out of four growing seasons, even among genotypes lacking full winter-hardiness, indicating significant production potential under appropriate management conditions. Optimal plant densities were identified within the range of 180–240 plants/m². From an economic standpoint, higher seeding densities appear justifiable, as the increased seed costs are offset by corresponding gains in yield. Since intercropping of lentils with oats did not negatively affect grain yield nor the thousand kernel weight, the benefits of this cropping system are highlighted. Sensory analysis demonstrated statistically significant differences in attributes such as mealiness and juiciness, leading to the classification of genotypes into three distinct sensory clusters. Despite these differences, overall sensory variation was relatively limited, suggesting that genotype selection may be guided primarily by agronomic performance, climatic adaptability, and winter-hardiness, as well as by market preferences for seed colour and size. Collectively, these results highlight the potential of autumn sowing as a viable strategy to enhance lentil production and reduce the risk of crop failure in Swiss agricultural systems. Full article

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

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19 pages, 1890 KB

Open AccessEditor’s ChoiceArticle

Machine Learning-Driven Prediction of Plant Water Potential in Kiwifruit Under Mediterranean Conditions

by Panagiotis Patseas, Anastasios Katsileros, Efthymios Kokkotos, Angelos Patakas and Anastasios Zotos

Agronomy 2026, 16(10), 1005; https://doi.org/10.3390/agronomy16101005 - 20 May 2026

Viewed by 444

Abstract

Kiwifruit (Actinidia deliciosa cv. Hayward) is a high-demand crop due to its nutritional value. Climate change increasingly challenges its cultivation, particularly under Mediterranean conditions, due to limited water resources. Therefore, the early detection of water stress onset is crucial for optimizing irrigation [...] Read more.

Kiwifruit (Actinidia deliciosa cv. Hayward) is a high-demand crop due to its nutritional value. Climate change increasingly challenges its cultivation, particularly under Mediterranean conditions, due to limited water resources. Therefore, the early detection of water stress onset is crucial for optimizing irrigation water use and enhancing kiwi productivity. In this context, advanced sensors capable of continuously monitoring critical hydrodynamic parameters, combined with machine learning approaches, offer a promising solution for reliable prediction of plant water status, supporting irrigation decision-making systems. This study develops and evaluates machine learning (ML) models to predict trunk water potential (Ψtrunk), integrating soil moisture, climatic variables, and plant-based measurements, including sap flow. Various machine learning models were evaluated including Ridge Regression, Lasso Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), using soil moisture, trunk water potential (Ψtrunk), sap flow, and microclimatic variables (relative humidity, wind speed, temperature, solar radiation, vapor pressure deficit, and reference evapotranspiration). Among the tested models, XGBoost demonstrated the best performance, achieving an accuracy of approximately 0.80, followed by Ridge, Lasso and SVM, which showed similar accuracy. Full article

(This article belongs to the Special Issue Crop Production in the Era of Climate Change)

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21 pages, 327 KB

Open AccessEditor’s ChoiceArticle

Growth and Yielding of Ten Cultivars of Plums (Prunus domestica L.) Grown in Organic System

by Agnieszka Głowacka, Witold Danelski and Elżbieta Rozpara

Agronomy 2026, 16(10), 1004; https://doi.org/10.3390/agronomy16101004 - 20 May 2026

Viewed by 367

Abstract

In recent years, Polish producers have been increasingly interested in organic fruit production. In this growing system, it is very important to choose cultivars that are less susceptible to diseases and pests. In a study conducted between 2016 and 2024 in central Poland, [...] Read more.

In recent years, Polish producers have been increasingly interested in organic fruit production. In this growing system, it is very important to choose cultivars that are less susceptible to diseases and pests. In a study conducted between 2016 and 2024 in central Poland, the suitability of ten plum cultivars (‘Cacanska Lepotica’, ‘Cacanska Najbolja’, ‘Kalipso’, ‘Katinka’, ‘Jubileum’, ‘Presenta’, ‘Silvia’, ‘Tophit’, ‘Tophit Plus’, and ‘Vision’) for organic cultivation was assessed. The results demonstrated that organic plum cultivation is feasible; however, it remains challenging. Based on long-term observations and experimental data, it was found that early-ripening plum cultivars such as ‘Katinka’, ‘Kalipso’ and ‘Cacanska Lepotica’ are more suitable for organic farming than others. Fruits of these cultivars were either not infested or only occasionally affected by the plum fruit moth and exhibited only sporadic symptoms of brown rot. The medium-early-ripening ‘Cacanska Najbolja’ cultivar is also worth noting, as the trees yield well in organic orchard conditions; however, the fruit is sometimes affected by rot-causing diseases and by the plum fruit moth. Fruits of late-ripening cultivars (‘Presenta’, ‘Tophit’, ‘Tophit Plus’, and ‘Vision’) were significantly more frequently infested by plum fruit moth caterpillars and exhibited a higher incidence of rot. The highest level of plum fruit moth infestation was observed in the fruit of ‘Tophit Plus’, whereas ‘Jubileum’ was the most susceptible to brown rot. These findings provide long-term evidence supporting cultivar selection as a key non-chemical strategy for improving the reliability of organic plum production under temperate climate conditions. The results may support cultivar selection strategies aimed at improving the sustainability and reliability of organic plum production in temperate climates. Full article

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

16 pages, 2233 KB

Open AccessEditor’s ChoiceArticle

Effects of Row Spacing and Nozzle Type on Spray Penetration Inside Soybean Canopy Under Various Wind Velocities

by Jose Theodoro, Heping Zhu, Hongyoung Jeon and Erdal Ozkan

Agronomy 2026, 16(10), 997; https://doi.org/10.3390/agronomy16100997 - 19 May 2026

Viewed by 365

Abstract

Adequate spray deposition and penetration of pesticides into the lower part of the soybean canopy can increase the chances of successfully protecting plants from diseases and insects. Only a small number of comprehensive studies have examined how spray application parameters (nozzle types, travel [...] Read more.

Adequate spray deposition and penetration of pesticides into the lower part of the soybean canopy can increase the chances of successfully protecting plants from diseases and insects. Only a small number of comprehensive studies have examined how spray application parameters (nozzle types, travel speed, droplet size, application rate, application equipment) affect droplet penetration into the inner and lower parts of the soybean canopy. However, the data obtained from replicated plots in these field experiments showed significant variability due to uneven soybean canopy characteristics and unpredictable wind speed and direction. To minimize variability in field studies, this study used a new methodology: conducting the experiment under controlled conditions in a wind tunnel. This research was conducted to evaluate the effect of increasing the distance between soybean rows on the spray coverage and deposition of different droplet size classes from various nozzles, delivering spray to the lower canopy in a wind tunnel. Four commercially available spray nozzles with droplet size classification from medium to extremely coarse were mounted on a spray boom with a spray controller to spray an application rate of 150 L ha⁻¹ under laminar wind speeds of 0, 2.4, and 5.1 m s⁻¹. Rectangular pots containing fully grown soybeans were placed in the test section of the tunnel at center-to-center distances of 0.38 and 0.76 m to replicate narrow and wide row spacings, respectively, commonly used by soybean growers. Eight points in each soybean row were selected to collect spray deposition and coverage with water-sensitive papers (WSPs) and acrylic plates (APs), respectively, at the top, middle, and lower layers of the canopy. Results showed that the top of the soybean canopy consistently received the highest amount of spray, regardless of application conditions, as expected, while the middle and lower layers of the canopy did not receive much spray. Nozzle types and wind speeds were not significant factors in increasing spray penetration into the middle to lower layers of soybean plants. Although wider row spacing improved the spray deposition in the lower part of the canopy, this improvement was not statistically significant. The main conclusions derived from this study indicate that even using wider row spacing configurations, spray penetration into the lower parts of the soybean canopy was limited due to denser canopy conditions and the effects of high wind speeds. Therefore, other advanced spray techniques, such as air-assisted spraying or using other mechanisms to expose lower parts of the canopy to the nozzles, may be needed to effectively overcome these limitations. Full article

(This article belongs to the Section Farming Sustainability)

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23 pages, 1134 KB

Open AccessEditor’s ChoiceArticle

An IDS-Compliant Agricultural Data Space Tailored to the Italian Context

by Francesco Camaccioli, Manlio Bacco, Gianluca Brunori, Federica Casarosa, Stefano Chessa and Alexander Kocian

Agronomy 2026, 16(10), 990; https://doi.org/10.3390/agronomy16100990 - 17 May 2026

Viewed by 467

Abstract

The digital transformation of agriculture has generated vast heterogeneous datasets from sensors, machinery, and administrative systems; however, interoperability and data sovereignty remain critical challenges. This study presents an IDS-compliant Agricultural Data Space tailored to the Italian context, integrating regulatory frameworks (General Data Protection [...] Read more.

The digital transformation of agriculture has generated vast heterogeneous datasets from sensors, machinery, and administrative systems; however, interoperability and data sovereignty remain critical challenges. This study presents an IDS-compliant Agricultural Data Space tailored to the Italian context, integrating regulatory frameworks (General Data Protection Regulation, Data Governance Act and Data Act) with the International Data Spaces (IDS) Reference Architecture Model. The study addresses key barriers to data sharing, including technical fragmentation, governance gaps, and economic incentives, by mapping Italian agricultural data flows onto the five-layer IDS model. Policy-based usage control is implemented through machine-enforceable Open Digital Rights Language policies, enabling farmer-centric data sovereignty. Three use cases, namely administrative Common Agricultural Policy (CAP) declarations, machine-generated data portability, and agri-food supply-chain traceability, demonstrate how structured and interoperable data exchange can reduce redundancy, mitigate vendor lock-in, and support sustainable agri-food systems. The findings highlight the feasibility of IDS-driven solutions in real-world agricultural ecosystems, emphasizing the need for sector-specific policy templates and scalable governance mechanisms. This work contributes to the development of the Common European Agricultural Data Space by bridging institutional, technical, and regulatory gaps. Full article

(This article belongs to the Special Issue Smart Agriculture: Cloud Data Control Platform)

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19 pages, 4299 KB

Open AccessEditor’s ChoiceArticle

Weed Management and Tobacco Production Are Influenced by Cropping Systems Including Cover Crops and Reduced Tillage

by Dylan Warren Raffa, Luisa del Piano, Eugenio Cozzolino, Tommaso Enotrio, Marco Quattrucci, Corrado Ciaccia and Luigi Morra

Agronomy 2026, 16(10), 989; https://doi.org/10.3390/agronomy16100989 - 17 May 2026

Viewed by 502

Abstract

Tobacco (Nicotiana tabacum L.) is an industrial crop cultivated worldwide with intensive management systems that include continuous cropping, conventional tillage and high use of agrochemicals. The increasing concerns about environmental and economic sustainability call for innovative practices to maintain yield while managing [...] Read more.

Tobacco (Nicotiana tabacum L.) is an industrial crop cultivated worldwide with intensive management systems that include continuous cropping, conventional tillage and high use of agrochemicals. The increasing concerns about environmental and economic sustainability call for innovative practices to maintain yield while managing weeds and enhancing soil fertility. Our research investigated the effect of green manure or cover crops coupled with minimum tillage on Kentucky tobacco production and the level of control of weeds. Six integrated management systems were tested in a four-year trial in Tuscany, Italy: (TS1) conventional farming management as defined above; (TS2) reduction in fertilizers and compost application; (TS3) rotation of tobacco–leguminous green manure and reduction in fertilizers; (TS4) rotation of tobacco–leguminous green manure and compost application without fertilizers; (TS5) rotation of tobacco–mixture of cover crops, minimum tillage before tobacco transplant, reduction in fertilizers; (TS6) as in TS5 but with a compost amendment addition. The different farming practices represented an ecological filter for the weed communities. The combination of conventional tillage, compost application and green manure was sufficient to control weed development. On the other hand, cover crop termination via roller crimper and minimum tillage did not reduce weed pressure, thereby negatively affecting tobacco production. Further studies are needed to improve the effectiveness of mulching and minimal tillage on weed levels not detrimental to tobacco development. It would be advisable to alternate different weed management strategies to prevent community specialization, mitigate negative effects on crops and enhance biodiversity at the farm scale. Full article

(This article belongs to the Special Issue Sustainable Agriculture: Plant Protection and Crop Production)

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15 pages, 1581 KB

Open AccessEditor’s ChoiceArticle

Quantifying Soil Organic Matter Effects on Nitrogen-Use Efficiency and Fate in Wheat–Maize Cropping Systems: A ¹⁵N Tracer Approach

by Lin Liu, Shulan Zhang, Xueyun Yang, Yinghua Duan, Xinhua He and Minggang Xu

Agronomy 2026, 16(10), 983; https://doi.org/10.3390/agronomy16100983 - 15 May 2026

Viewed by 566

Abstract

Soil organic matter (SOM) is a recognized determinant of nitrogen-use efficiency (NUE), but its quantitative control over the fate of fertilizer N remains unclear. Using a ¹⁵N tracer study within a winter wheat–summer maize region, we quantified the recovery of initially applied [...] Read more.

Soil organic matter (SOM) is a recognized determinant of nitrogen-use efficiency (NUE), but its quantitative control over the fate of fertilizer N remains unclear. Using a ¹⁵N tracer study within a winter wheat–summer maize region, we quantified the recovery of initially applied N from wheat and subsequent crops in relation to SOM and N application rates. We found that while N fertilization boosted yields by 85–340% in low-fertility soil, its effectiveness exhibited diminishing returns in high-fertility soils. Crucially, the total recovery efficiency of fertilizer N (cumulative ¹⁵NUE) across three cropping seasons was fundamentally governed by SOM content, following a linear–plateau relationship. The model revealed that the maximum ¹⁵NUE (54.3%) at an optimal application rate (105 kg N ha⁻¹) was achieved when SOM exceeded a critical threshold of 21.3 g kg⁻¹ (equivalent to 57.51 t ha⁻¹ in the 0–20 cm soil layer). Below this threshold, ¹⁵NUE increased linearly with SOM (R² = 0.956). Furthermore, residual ¹⁵N in soil was primarily stabilized in organic forms (58–64%), while recovery by subsequent crops was minimal (≤4.3%). This confirms that high SOM content minimizes the amount of unaccounted ¹⁵N by enhancing N fixation within the soil organic pool. Our findings establish a quantifiable SOM threshold for maximizing NUE, thereby providing a scientific basis for reducing fertilizer waste and enhancing the sustainability of intensive agriculture in the region. Full article

(This article belongs to the Special Issue Integrated Nitrogen Management for Improved Use Efficiency in Agroecosystems)

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25 pages, 2282 KB

Open AccessEditor’s ChoiceArticle

Crop Yield Responses to Reduced Solar Radiation in Agrivoltaic Systems: Crop-Specific Patterns and Shading Thresholds

by Aditi Jha, Greta Heiser, Robert Kelvey and Qimin Huang

Agronomy 2026, 16(10), 985; https://doi.org/10.3390/agronomy16100985 - 15 May 2026

Viewed by 889

Abstract

Crop yield responses to reduced solar radiation are central to the design of agrivoltaic systems, yet crop-specific patterns and critical shading thresholds remain insufficiently characterized across diverse environments. This study evaluates yield responses across a global dataset of 546 observations from 66 studies, [...] Read more.

Crop yield responses to reduced solar radiation are central to the design of agrivoltaic systems, yet crop-specific patterns and critical shading thresholds remain insufficiently characterized across diverse environments. This study evaluates yield responses across a global dataset of 546 observations from 66 studies, including agrivoltaic, shading, and agroforestry systems. Relative yield was analyzed in relation to reduction in solar radiation (RSR), crop type, and environmental variables using exploratory analysis, multiple linear regression, and tree-based ensemble models. Crop responses varied systematically across crop types. Fruits, berries, and fruity vegetables maintained or increased yield under lower shading levels, while forages, leafy vegetables, cereals, and tubers showed gradual declines, and maize and grain legumes exhibited the strongest sensitivity. Across models, yield responses were non-linear, with relatively stable yields at lower shading levels followed by accelerated declines beyond approximately 50–60% RSR. Climatic conditions further influenced these patterns, with crops in higher-radiation and warmer environments maintaining yields more effectively under partial shade. These findings demonstrate that crop yield responses depend on crop type, shading intensity, and environmental context, providing an agronomic basis for crop selection and agrivoltaic system design. Full article

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

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36 pages, 19416 KB

Open AccessEditor’s ChoiceArticle

Spatial, Temporal, and Vertical Variability of Greenhouse Microclimate and Artificial Neural Network-Based Prediction Under Korean Summer and Winter Conditions

by Md Nasim Reza, Md Razob Ali, Hongbin Jin, Sakib Robin, Md Aminur Rahman, Hyeunseok Choi and Sun-Ok Chung

Agronomy 2026, 16(10), 960; https://doi.org/10.3390/agronomy16100960 - 12 May 2026

Viewed by 671

Abstract

Understanding greenhouse microclimatic variability is essential for precise environmental monitoring and control. This study evaluated temperature, relative humidity, CO₂ concentration, and light intensity variability in Korean greenhouses during summer and winter, and developed artificial neural network (ANN) models to predict indoor temperature [...] Read more.

Understanding greenhouse microclimatic variability is essential for precise environmental monitoring and control. This study evaluated temperature, relative humidity, CO₂ concentration, and light intensity variability in Korean greenhouses during summer and winter, and developed artificial neural network (ANN) models to predict indoor temperature and relative humidity at different layers. A glass greenhouse and an arched-frame double-layer plastic greenhouse were monitored during summer and winter, respectively. A wireless sensor network was deployed at multiple spatial positions and vertical layers, and layer-specific artificial neural network (ANN) models were developed to predict indoor temperature and relative humidity at the top, middle, and bottom layers. The measured results revealed clear temperature and humidity stratification, with the top layer generally showing a higher temperature and lower humidity than the middle and bottom layers. In summer, temperatures reached 36.4 °C, while relative humidity ranged from 55% to 92%, while in winter, temperature varied from 3.4 °C to 35.0 °C and relative humidity ranged from 73% to 91%. Spatial contour mapping showed clear microclimatic gradients, and ANOVA with Tukey’s HSD tests confirmed significant differences among sensor locations (p < 0.05). The ANN models predicted indoor temperature with high accuracy, with R² values generally above 0.95, while humidity prediction showed larger errors. Full article

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

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31 pages, 948 KB

Open AccessEditor’s ChoiceReview

An Ecosystem Framework for Tomato Precision Agriculture: Integrating Measurement, Understanding, Optimization, Prediction, and Diagnosis

by Sangyoon Lee, Hongseok Mun, Joonmo Kang and Byeongeun Moon

Agronomy 2026, 16(10), 965; https://doi.org/10.3390/agronomy16100965 - 12 May 2026

Viewed by 436

Abstract

Tomato (Solanum lycopersicum L.) production faces increasing pressure from resource scarcity and climate change, creating demand for more precise and adaptive management. However, adoption in commercial systems remains limited because many advanced technologies are costly, poorly interoperable, or difficult for growers to [...] Read more.

Tomato (Solanum lycopersicum L.) production faces increasing pressure from resource scarcity and climate change, creating demand for more precise and adaptive management. However, adoption in commercial systems remains limited because many advanced technologies are costly, poorly interoperable, or difficult for growers to interpret. This review addresses that gap by organizing recent advances into a five-stage production ecosystem framework: Measurement, Understanding, Optimization, Prediction, and Diagnosis. Unlike previous precision agriculture reviews that mainly summarize sensing, modeling, artificial intelligence, and robotics as separate topics, this framework emphasizes stage-linked integration and decision support relevance across practical tomato production. Measurement establishes the data foundation through sensor networks and imaging; Understanding converts observations into physiological insight using process-based models; Optimization applies these insights to water, nutrient, and microclimate management. Prediction uses machine learning and explainable artificial intelligence to anticipate yield, quality, and stress responses, while Diagnosis supports timely disease detection and vision-based intervention. Overall, this review shows that progress in tomato precision agriculture depends less on isolated algorithmic advances than on cost-effective, modular, interpretable, and operationally feasible systems for commercial deployment. Full article

(This article belongs to the Collection AI, Sensors and Robotics for Smart Agriculture)

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23 pages, 3987 KB

Open AccessEditor’s ChoiceArticle

UAV-Based Multi-Source Feature Fusion and Ensemble Learning for Maize Growth Monitoring and Fertilizer Optimization in Saline–Alkali Regions

by Xun Yang, Haixiao Ge, Fenfang Lin, Fei Ma and Changwen Du

Agronomy 2026, 16(10), 951; https://doi.org/10.3390/agronomy16100951 - 11 May 2026

Cited by 1 | Viewed by 611

Abstract

In saline–alkali environments, soil salinity imposes severe abiotic stress on maize growth by inhibiting root activity and nutrient uptake. Traditional destructive sampling methods struggle to enable cross-growth stage, large-scale dynamic fertilizer effect assessment. This study, conducted in saline–alkali farmlands of Inner Mongolia, utilized [...] Read more.

In saline–alkali environments, soil salinity imposes severe abiotic stress on maize growth by inhibiting root activity and nutrient uptake. Traditional destructive sampling methods struggle to enable cross-growth stage, large-scale dynamic fertilizer effect assessment. This study, conducted in saline–alkali farmlands of Inner Mongolia, utilized UAV multispectral remote sensing to extract 20 vegetation indices and 40 texture parameters, constructing a multi-source feature set. An ensemble learning framework integrating Random Forest (RF), Decision Tree (DTR), AdaBoost and Gradient Boosting Regression (GBR) was developed to achieve precise monitoring of maize plant height, leaf area index (LAI), and yield. In addition, the study aimed to evaluate the dynamic effects of seven fertilizer treatments (six controlled-release composite fertilizers, T1–T6, and conventional CK) and to identify the optimal fertilization scheme, with particular emphasis on comparing the two best-performing treatments, T1 and T2. Results showed that: (1) The ensemble model improved prediction robustness, with R² values of 0.88, 0.76, and 0.76 for plant height, LAI, and yield across the entire growth cycle, respectively. The integration of texture features effectively mitigated spectral saturation during peak growth stages (e.g., tasseling and filling). (2) For fertilizer evaluation, T1 performed best in growth and yield at jointing, tasseling, and filling stages, with a yield increase rate of up to 40.18% at the jointing stage. Although T2 slightly outperformed T1 in yield increase at maturity (15.42%), T1 was identified as the optimal fertilizer scheme for the region based on whole-growth-stage growth performance, measured yield, LAI, and yield increase rate. These results demonstrate that UAV-based multi-source feature fusion combined with ensemble learning provides an effective and non-destructive approach for fertilizer evaluation and precision nutrient management in saline–alkali regions. Full article

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

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25 pages, 11007 KB

Open AccessEditor’s ChoiceReview

Population-Based Threshold Models for Predicting Weed Emergence: A Synthesis as a Conceptual Framework for the Development of Tools for Site-Specific Management

by Cristian Malavert, Diego Batlla and Roberto L. Benech-Arnold

Agronomy 2026, 16(10), 948; https://doi.org/10.3390/agronomy16100948 - 8 May 2026

Viewed by 873

Abstract

Effective weed management is crucial for optimizing agricultural productivity and minimizing environmental impacts. Weeds are most effectively managed during their seedling or early growth stages, which can be achieved with the aid of tools for predicting seedling emergence. However, many persistent weed species [...] Read more.

Effective weed management is crucial for optimizing agricultural productivity and minimizing environmental impacts. Weeds are most effectively managed during their seedling or early growth stages, which can be achieved with the aid of tools for predicting seedling emergence. However, many persistent weed species exhibit dormant seedbanks, thus complicating prediction attempts. The number of seedlings emerging in these species is closely tied to seedbank dormancy levels, which are influenced by seasonal variations. Thus, predictive population-based threshold models incorporate seedbank dormancy regulation to accurately forecast seedling “window” emergence. These models use the functional relationship between environmental cues (i.e., temperature, light, alternating temperatures, and soil water content) and seed dormancy behavior. Considering that these environmental signals vary among microsites in the field, these tools can be adapted to predict weed emergence in both temporal and spatial dimensions, thus making them suitable for site-specific weed management. The aim of this review is to synthesize existing modeling approaches and present a conceptual framework for dynamic, site-specific weed emergence predictions, supported by case-study-based applications. The illustrative application shows that incorporating soil water content into dormancy dynamics modifies emergence timing and magnitude, restricting emergence to specific topographic zones and potentially reducing herbicide use by up to 60–70%. This approach can improve the efficiency of herbicide applications and other control measures, reducing costs and environmental impact while enhancing crop yields. This work underscores the potential of integrating environmental cues into sophisticated modeling approaches to address the complexities of weed emergence in diverse agricultural landscapes. Full article

(This article belongs to the Special Issue State-of-the-Art Research on Weed Populations and Community Dynamics)

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24 pages, 7190 KB

Open AccessEditor’s ChoiceArticle

DNA Barcoding and Allele-Specific PCR Discrimination of Glasswort Ecotypes from Apulia Region (Southern Italy)

by Angelica Giancaspro, Giulia Conversa, Luigi Giuseppe Duri, Gaetana Ricatti, Antonio Elia, Stefano Pavan and Concetta Lotti

Agronomy 2026, 16(10), 947; https://doi.org/10.3390/agronomy16100947 - 8 May 2026

Viewed by 454

Abstract

In the scenario of ongoing climate changes, the selection of plant genotypes with high salt tolerance is emerging as the most sustainable strategy to safeguard crop yield and quality and make productive use of salinized soils. Glassworts are annual and perennial halophytes found [...] Read more.

In the scenario of ongoing climate changes, the selection of plant genotypes with high salt tolerance is emerging as the most sustainable strategy to safeguard crop yield and quality and make productive use of salinized soils. Glassworts are annual and perennial halophytes found in inner and coastal wastelands, indistinctly consumed as high-nutritional green vegetables. Traditional taxonomic classification based on morphological traits can be very challenging in glasswort, due to phenotypic plasticity, reduced plant morphology, and inbreeding. In this work, we used DNA-based molecular tools to overcome such constraints and assess inter-generic and inter-specific genetic diversity in a collection of ecotypes from different Apulian areas. A fast and reliable Allele-Specific PCR assay was optimized to enable molecular detection of annual and perennial genera. Species-level classification was obtained through a similarity- and phylogeny-based approach relying on matK and rbcL DNA barcoding. Combined DNA tools identified perennial samples as Sarcocornia fruticosa and Arthrocaulon macrostachyum, along with annual Salicornia europaea, and phylogenetic trees unveiled genetic distances between glassworts, which clustered according to life cycle. The relationship between genotypes and nutritional profiles was finally investigated, suggesting that environmental factors may play a predominant role over taxonomic relatedness in shaping interspecific differences in nutrient composition of the analyzed samples. Full article

(This article belongs to the Special Issue Agro-Environmental Sustainable Exploitation of Halophyte, Medicinal and Aromatic Species from Marginal Areas—2nd Edition)

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27 pages, 2382 KB

Open AccessEditor’s ChoiceArticle

Evaluating Photochemical Efficiency and Recovery Potential in Wheat Varieties with Divergent Drought Tolerance

by Vladimir Aleksandrov, Dilyana Doneva, Svetlana Misheva, Katelina Prokopova, Alexander Angelov and Violeta Peeva

Agronomy 2026, 16(10), 944; https://doi.org/10.3390/agronomy16100944 - 8 May 2026

Viewed by 782

Abstract

Drought stress during early growth stages severely limits wheat productivity globally. Understanding varietal physiological responses to drought stress is critical for breeding climate-resilient cultivars. Two-week-old plants from two winter wheat (Triticum aestivum L.) cultivars—Katya (drought-tolerant) and Zora (drought-sensitive)—were subjected to drought for [...] Read more.

Drought stress during early growth stages severely limits wheat productivity globally. Understanding varietal physiological responses to drought stress is critical for breeding climate-resilient cultivars. Two-week-old plants from two winter wheat (Triticum aestivum L.) cultivars—Katya (drought-tolerant) and Zora (drought-sensitive)—were subjected to drought for seven days, followed by rehydration. The experiments were conducted in pots in controlled conditions. The photosystem II (PSII) function was evaluated using chlorophyll a fluorescence (OJIP transients), thermoluminescence emissions and pigment content analysis. Under drought, Katya maintained functional PSII integrity with stable quantum efficiency and increased chlorophyll content, while Zora exhibited chlorophyll degradation. Fresh and dry weight declined in both genotypes but significantly only in Zora; recovery occurred after rehydration. Chlorophyll fluorescence revealed that varietal divergence was localized to the O–J phase of PSII photochemistry, indicating differences in reaction-center behavior confirmed by thermoluminescence. Katya demonstrated preserved PSII reaction-center density, balanced energy partitioning, homogeneous PSII populations, and superior recovery capacity. Conversely, Zora showed reaction-center depletion, elevated energy dissipation, impaired electron transport beyond Q_A, and persistent PSII heterogeneity even after rehydration. Drought tolerance in the studied genotypes was associated with the maintenance of PSII structural integrity, efficient photochemical function, and rapid recovery mechanisms. These physiological markers—particularly early PSII photochemistry kinetics and reaction-center stability—provide valuable selection criteria for breeding programs, targeting drought resilience under changing climate conditions. Full article

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

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