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        <title>Frontiers in Agronomy | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/agronomy</link>
        <description>RSS Feed for Frontiers in Agronomy | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-04-15T20:36:13.510+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1768276</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1768276</link>
        <title><![CDATA[Sustainable agriculture from the bottom up: a compact and versatile low-cost platform for agricultural monitoring]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Milan Kretzschmar</author><author>Maren Dubbert</author><author>Matthias Lück</author><author>Michael Asante</author><author>Geoffroy Sossa</author><author>Mathias Hoffmann</author>
        <description><![CDATA[IntroductionDigital technologies in agriculture are often associated with sophisticated, high-end, or network-capable systems based on robotics, remote sensing, or IoT. These systems, however, remain financially and logistically inaccessible for many researchers and farmers, particularly in remote regions and the Global South. This paper presents a contrasting, yet complementary, bottom-up approach by advocating the adoption of low-cost DIY systems to enable site-specific crop management and support climate-adaptive decisions.MethodsThe Environmental Variables Explorer (EVE) is presented as a low-cost, open-source platform that integrates modular microcontroller-based sensing, reliable low-power operation, timekeeping, and non-volatile data storage. The platform supports two interoperable, end-to-end workflows under full user control: EVE-Offline, a stand-alone logger that stores measurements locally in FRAM and allows wireless data retrieval via a custom Android Bluetooth app; and EVE-Online, an ESP32-based node that transmits data via Wi-Fi to a self-hosted backend providing a web dashboard and CSV export on inexpensive shared hosting. To demonstrate the platform, EVE was implemented in both workflows as a compact weather station recording photosynthetically active radiation (PAR), air temperature, relative humidity (RH), and air pressure at user-defined intervals. Sensor performance was evaluated using co-location tests and independent reference stations.ResultsThe custom low-cost PAR sensor showed strong reproducibility and close agreement with reference measurements. A co-location validation against a TOMST TMS-4 reference confirmed near 1:1 temperature agreement under identical conditions. Field deployments further demonstrated stable temporal dynamics across variables and reliable end-to-end data handling in both offline and online workflows.DiscussionThrough its openly accessible hardware designs, firmware, backend code, and build instructions, EVE provides a practical alternative to locked-in commercial systems. It enables cost-effective, bottom-up monitoring for researchers, farmers, educators, and community initiatives, while its modular platform can be extended with additional sensors and locally validated analyses without changing the underlying workflows. These results show that improving agricultural productivity while minimizing environmental impacts requires not only cutting-edge, data-heavy technologies but also autonomous, customizable tools that support user-driven innovation from the bottom up, particularly in resource-limited contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1775298</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1775298</link>
        <title><![CDATA[Crop simulation-based assessment of yield gaps and water productivity for improving blackgram (Vigna mungo L.) production across diverse ecologies of India]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>P. G. Dodewar</author><author>P. S. Brahmanand</author><author>Subash N. Pillai</author><author>S. K. Srivastava</author><author>R. S. Bana</author><author>V. K. Prajapati</author><author>Bharadwaj Chellapilla</author><author>Susama Sudhishri</author><author>Twinkle Jena</author>
        <description><![CDATA[Blackgram (Vigna mungo L.), a nutritionally and ecologically important pulse crop in India, remains constrained by climatic stress, moisture limitations, and suboptimal agronomic management. To identify pathways for sustainable intensification, the APSIM model was employed to simulate potential yield (Yp), quantify yield gaps, and assess water productivity across twenty major blackgram-growing districts of India during 2003-2023. Simulations integrated long-term weather data, soil profiles, region-specific management practices, and cultivar-specific genetic coefficients, while water productivity was evaluated using physical (PWP), irrigation (IWP), and economic (EWP) indices. Observed yields (280–1040 kg ha-1) were consistently lower than simulated potential yields (840–1176 kg ha-1), revealing yield gaps ranging from 5 to 67%, with an average gap of about 515 kg ha-1. Central Zone districts exhibited the widest gaps, whereas rice-fallow systems in the South Zone showed relatively higher productivity and economic water returns. Model performance was robust, with high accuracy during calibration and validation (R2 and NSE ≈ 0.97-0.99; RMSE ≤20%; NMSE<0.05). District-level carbon budgeting revealed blackgram to be a consistent net carbon sink, with carbon efficiency ranging from 7.05-9.69 and a mean CSI of 7.21. Principal Component Analysis identified nutrient-water coupling (N, P, and irrigation), soil organic carbon, and yield components as dominant drivers of yield variability, while correlation-density matrix analysis highlighted strong interdependence among reproductive traits and trade-offs with crop geometry. Bridging yield and water productivity gaps through climate-resilient varieties, optimized crop geometry, precision irrigation, and targeted policy support can substantially enhance blackgram productivity and resource-use efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1771721</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1771721</link>
        <title><![CDATA[Agronomic practices for climate-resilient okra production in agroecological systems: a review]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Samuel Mathu Ndungu</author><author>Lokeshwar Kesamreddy</author><author>Mathieu A. T. Ayenan</author><author>Stephen Othim</author><author>Siyabusa Mkuhlani</author><author>Yu-Hsiang Liu</author><author>Amha Besufkad</author><author>Wubetu Bihon Legesse</author><author>Eric C. Legba</author><author>Judith Honfoga</author><author>Lukas Pawera</author>
        <description><![CDATA[Okra is increasingly recognized as a climate-resilient crop within diversified agroecological systems; however, an integrated synthesis of its biophysical, agronomic, and socioeconomic dimensions is lacking. This review consolidates the current knowledge on okra physiology, production ecology, and value chains, with a focus on climate resilience under heat, drought, salinity, pest, and disease pressures. Evidence across regions demonstrates that agroecological practices such as crop diversification, mulching, conservation tillage, integrated pest management, and the use of organic and biological inputs consistently improve soil health, stabilize yields under climatic stress, and reduce dependence on external inputs, although with trade-offs in labor demand and short-term productivity. The second key finding is that enabling technologies, including remote sensing, crop modeling, and digital advisory platforms, can enhance climate risk management for okra systems when adapted to smallholder contexts, while unresolved challenges remain related to data quality, accessibility, and institutional support. Third, persistent socioeconomic and policy constraints, notably weak seed systems, limited credit access, and insufficient extension capacity, continue to restrict the large-scale adoption of resilient practices. The novelty of this review lies in its integrated agroecological climate-smart framework that links biophysical constraints, management trade-offs, digital innovations, and policy barriers into a unified roadmap. Based on this synthesis, priority research and policy actions are proposed, including targeted genetic improvement, climate impact assessment, sustainable intensification, labor-saving mechanization, post-harvest value addition, nutrition-sensitive interventions, and inclusive market development, positioning okra as a strategic crop for resilient and equitable food systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1819891</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1819891</link>
        <title><![CDATA[Global meta-analysis reveals soil, climate, and management drivers of crop yield and nitrogen use efficiency under enhanced-efficiency fertilizers]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xinping Zhang</author><author>Zerong Wang</author><author>Linzhuan Song</author><author>Xinrong Duan</author><author>Yichen Wang</author><author>Li Zhao</author><author>Xuefeng Zhao</author><author>Ben Niu</author><author>Lijie Hao</author><author>Zengzeng Yang</author><author>Chuangyun Wang</author>
        <description><![CDATA[Improving nitrogen use efficiency (NUE) while maintaining high crop yields is a key challenge for achieving green and sustainable agricultural development. Enhanced-efficiency fertilizers (EENFs) have broad potential to improve crop yield and nitrogen use efficiency (NUE), yet their responses across diverse climatic, soil, and management conditions remain poorly quantified. Here, we present a global meta-analysis of field studies evaluating the effects of controlled-release urea (CRU), nitrification inhibitors (NI), urease inhibitors (UI), and their combination (NI+UI) on crop yield and NUE. EENFs increased average crop yield by 8.32% and NUE by 25.90%, with NUE consistently exhibiting greater relative gains than yield across treatments. Effect magnitudes varied by fertilizer type: NI enhanced yield by 7.69-10.44%; CRU increased NUE by 25.60%; NI+UI improved NUE by 34.00%; and UI alone increased NUE by 26.55%. Climate strongly modulated these effects. Yield responses to MAT and MAP were relatively stable, whereas NUE was highly sensitive to environmental gradients. Notably, NUE gains under NI+UI peaked at intermediate temperatures (7.5-15 °C), and yield responses were largest under MAP >1200 mm. Soil properties, including soil organic carbon, total nitrogen, and pH, further influenced both yield and NUE, with NI+UI showing the highest responsiveness in low-fertility soils. Crop type, fertilization frequency, and nitrogen application rate also modulated fertilizer effectiveness, with UI most effective for wheat and maize yield, and NI+UI consistently enhancing NUE in rice and other crops. Model analyses revealed that the relative importance of environmental, soil, and management factors differed among fertilizer types, with crop variety, soil nutrients, pH, and fertilization practices as key predictors. Collectively, EENFs reliably enhance crop yield and NUE across global agroecosystems, yet their efficacy is context-dependent, driven by the interaction of climate, soil, crop, and management factors. These findings provide quantitative guidance for optimizing fertilizer use to improve agricultural sustainability and reduce nitrogen losses.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1812482</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1812482</link>
        <title><![CDATA[Bridging soybean yield gaps in Ghana: opportunities and challenges for sustainable smallholder production and livelihood]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>George A. Awuni</author><author>Peter A. Asungre</author><author>George Y. Mahama</author><author>Jerry A. Nboyine</author><author>Francisca Addae-Frimpomaah</author><author>Francis Kusi</author><author>Darrin M. Dodds</author><author>Daniel B. Reynolds</author>
        <description><![CDATA[IntroductionSoybean, Glycine max (L.) Merr., is a globally important legume that supports food security, nutrition, and sustainable agricultural systems. Compared with cereal crops, soybean improves soil fertility through biological nitrogen fixation, induces suicidal Striga germination, reduces greenhouse gas emissions, and enhances resource-use efficiency. Despite favorable agroecological conditions, Ghana’s soybean production remains well below its potential.Method and materialThis research systematically reviewed published literature on global and subregional soybean production, harvested area, and yield to identify gaps and limitations in Ghana’s soybean production systems.ResultsIn Sub-Saharan Africa, soybean production is increasing primarily driven by harvested area expansion rather than yield improvement, with yield gap still lagging global averages by 46%. In Ghana, the annual soybean production is estimated at 300, 000 - 350, 000 tons, which represent about 26% of the country’s production potential, with a national demand exceeding 600, 000 tons. The national average yield is estimated at 1.85 t ha-1 while the potential yield is estimated at 3.0 tons per hectare. Ghana experiences gaps of 49%, 22%, and 38% in production, area harvested, and yield, respectively. The supply deficit drives import dependence and vulnerability to global price fluctuations. Key constraints identified include climate variability, limited cultivar diversity, low mechanization, suboptimal agronomic practices, weak market integration, and inadequate policy support.DiscussionGlobal soybean production has achieved relatively high yields driven by widespread adoption of improved technologies, advanced breeding programs, mechanization, and precision agronomic practices. However, yields in Sub-Saharan Africa and Ghana, remain substantially lower, highlighting persistent yield gap. Addressing this gap requires a multi-pronged strategy. Support for breeding programs for improved, high-yielding and stress-tolerant cultivars, climate-smart agronomic practices, conservation agriculture, and integrated soil fertility management. Supportive policies such as increased access to appropriate mechanization, strengthened agricultural extension and advisory services, market frameworks that incentivize production and reduce risks for smallholder farmers are required.ConclusionClosing Ghana’s soybean yield gap is critical given rising population, dietary shifts toward plant-based proteins, and expansion of the feed industry. Addressing yield gap limitations can enhance productivity, reduce import reliance, promote sustainable livelihoods, support economic growth, and contribute to a resilient and sustainable food system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1804737</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1804737</link>
        <title><![CDATA[Foliar application of salicylic acid enhances yield, oil quality and economic returns of Linum usitatissimum L. under different irrigation regimes]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Manjeet Singh</author><author>Gurudev Singh</author><author>Sakshi Goyat</author><author>Sandeep Gawdiya</author><author>Sindhu Sheoran</author><author>Eetela Sathyanarayana</author>
        <description><![CDATA[Background/PurposeLinseed (Linum usitatissimum L.) is an important oilseed crop, but its productivity is frequently limited by water stress during critical growth stages. Salicylic acid (SA) has been reported to improve stress tolerance and crop performance. However, the interaction between irrigation regimes and SA application and their combined influence on yield, seed quality and economic returns of linseed under field conditions remain insufficiently explored. This study aimed to evaluate the effects of different irrigation regimes and foliar applications of SA on yield attributes, oil quality and economic profitability of linseed cultivation.MethodsA field experiment was conducted during the rabi season of 2024-25 at the Research Farm of Chaudhary Sarwan Kumar Himachal Pradesh Krishi Vishvavidyalaya, Palampur, India. The experiment was arranged in a strip-plot design with three replications. Three irrigation regimes were evaluated: IR1 (rainfed), IR2 (two irrigations at branching and pre-flowering stages) and IR3 (three irrigations at branching, pre-flowering and capsule development stages). Four foliar treatments of salicylic acid were applied at branching and pre-flowering: S1 (water spray), S2 (50 mg/L), S3 (75 mg/L) and S4 (100 mg/L). Yield attributes, seed yield, oil content, oil yield and economic returns were recorded and analysed statistically.ResultsBoth irrigation and salicylic acid significantly influenced yield and quality traits of linseed. The highest seed yield (1557 kg ha-1), oil content (37.2%), oil yield (580 kg ha-1), net return (USD 980 ha-1) and benefit cost ratio (2.24) were obtained under IR3. Among SA treatments, 100 mg/L produced the highest seed yield (1467 kg ha-1), oil yield (547 kg ha-1), net return (USD 905 ha-1) and B:C ratio (2.11). The interaction of IR3 × S4 resulted in the maximum seed yield (1624 kg ha-1) and oil yield (618 kg ha-1). However, 75 mg/L SA showed nearly comparable performance to 100 mg/L, indicating that moderate concentrations can achieve efficient yield improvement.ConclusionsStrategic irrigation at branching, pre-flowering and capsule development stages combined with foliar application of salicylic acid significantly improves linseed productivity, oil yield and farm profitability. Although 100 mg/L SA produced the highest yields, 75 mg/L was identified as the most efficient concentration due to comparable performance with potentially lower input requirements. These findings highlight the potential of integrating optimized irrigation scheduling with salicylic acid application as a climate-smart strategy for sustainable linseed production under variable moisture conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1795379</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1795379</link>
        <title><![CDATA[Phosphorus and potassium interaction in maize: enhancing yield and soil health under Nepalese agroecosystems]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Som Nepali</author><author>Rajesh Tamang</author>
        <description><![CDATA[Phosphorus (P) and potassium (K) are essential nutrients for maize growth and productivity, but their imbalanced application limits yield and soil fertility in Nepalese mid-hills. This study evaluated the interactive effects of P and K on maize yield, physiological responses, and residual soil nutrients to develop site-specific nutrient management strategies. The experiment was conducted in Kaski District, using a split-plot design with four P levels (0, 50, 100, and 150 kg P2O5 ha−1) as main plots and three K levels (0, 25, and 50 kg K2O ha−1) as subplots, replicated three times. Maize variety Rampur Hybrid-10 was grown under standard crop management practices. Growth, yield components, tissue P and K content, and post-harvest soil-fertility were measured. Results showed that P significantly increased grain yield, biomass, straw yield, and thousand-grain weight, with the highest grain yield (5,820 kg ha−1) at 150 kg P2O5 ha−1. K also improved grain yield, with maximum yield (5,430 kg ha−1) at 50 kg K2O ha−1. A synergistic effect was observed under combined P150 × K50, resulting in 6,120 kg ha−1 grain yield. Tissue nutrient analysis confirmed enhanced P and K uptake with balanced fertilization. Residual soil P and K increased under higher P and K applications, while soil pH and organic matter remained stable. The study demonstrates that balanced P and K management improves maize productivity and supports soil fertility. Site-specific nutrient application can reduce yield gaps and promote sustainable maize production in Nepal. Long-term trials are recommended to validate these findings across diverse environmental conditions. This study is based on original field experimentation and laboratory analysis conducted under Nepalese mid-hill agroecosystem conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1777087</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1777087</link>
        <title><![CDATA[Evaluation of YOLO-based weed detection models on commercial horseradish fields in Southern Illinois]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abhinav Pagadala</author><author>Sandesh Poudel</author><author>Janmejay Umakanth Rathi</author><author>S. Sunoj</author><author>John F. Reid</author>
        <description><![CDATA[Horseradish (Armoracia rusticana) is a high-value specialty crop whose production in Southern Illinois is constrained by limited herbicide options and labor-intensive weed management, particularly in commercial fields. This study developed a multisource image dataset and evaluated lightweight deep learning models to enable real-time, vision-based weed detection for future robotic weeding systems in commercial horseradish fields of Southern Illinois. Image data collection was conducted during the 2024 growing season at two commercial fields and one research site using a handheld smartphone and an unmanned ground vehicle (Farm-ng Amiga robotic platform) equipped with two stereo cameras (Luxonis OAK-D cameras). The data was first annotated, assigning appropriate labels to horseradish and weed instances, then augmented to improve data diversity. We trained and compared nine YOLO models (v8, v11, v12; nano, small, medium) using standard object detection metrics (precision, recall, F1-score, mAP@50) and computational indicators (inference time, GFLOPs, model size, training time), and selected the best configuration for hyperparameter tuning with an automated search over learning rate, regularization, and optimizer. The tuned YOLOv8-nano model achieved the best balance of detection performance and computational efficiency, and was subsequently benchmarked on multiple desktop and edge-computing platforms to assess real-time feasibility. The results demonstrated that lightweight YOLO architectures can provide accurate, fast horseradish-weed detection suitable for deployment on embedded hardware, offering a key sensing component for future autonomous mechanical weeding in commercial horseradish production. This study makes three key contributions: (i) a multisource image dataset of horseradish and weeds collected from both commercial and research fields using manual imaging and a robotic platform; (ii) an evaluation protocol that combines accuracy metrics with computational indicators to guide model selection for embedded deployment; and (iii) a cross-platform benchmarking workflow that assesses the real-time feasibility of lightweight YOLO models on desktop and edge-computing hardware for robotic weeding applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1775355</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1775355</link>
        <title><![CDATA[Screenhouse-based shade-tolerance assessments of coffee and bean varieties, and cover crops, to optimize multi-strata intercropping in African smallholder systems]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jules Ntamwira</author><author>Walter Ocimati</author><author>Elizabeth Kearsley</author><author>Telesphore C. Mirindi</author><author>Gabriel Shabani</author><author>Guy Blomme</author>
        <description><![CDATA[IntroductionIntercropping annuals with perennials such as banana, coffee and trees is widespread in East and Central Africa, where farms are typically small and fragmented. Within such multi-strata production systems, shade is a critical constraint affecting crop productivity. This study aimed to identify crop species and varieties tolerant to varying shade levels.MethodsTwenty common bush and climbing bean (Phaseolus vulgaris) varieties each, eight Arabica coffee (Coffea arabica) varieties, and three cover crops (velvet bean (Mucuna pruriens), rattle pod (Crotalaria grahamiana), and elephant grass (Pennisetum purpureum)) were screened under four screenhouses transmitting photosynthetically active radiation (PAR) levels of 70%, 50%, 35% and 10% compared to open field light levels.ResultsEight bush and four climbing bean varieties maintained grain yields comparable to open-field conditions at low (70% light transmission) shade conditions. High tolerance persisted for many beans at 50% and 35% transmission, though only two bush beans (HM21-7, CODMLB 499) remained unaffected at the deepest shade (10% light transmission). Bush beans consistently elongated under shade, whereas climbing beans showed mixed height responses. Arabica coffee seedlings exhibited stable height and stem diameter across all shade levels, with only a few varieties showing modest reductions. Cover crops showed no shade tolerance, with > 84% above-ground biomass loss across all shade levels for elephant grass. Meanwhile velvet bean and rattle-pod suffered 48–58% biomass losses even at 70% light transmission, though velvet bean retained a high root mass until the deepest shade.DiscussionThese findings suggest a high diversity in shade tolerance within existing bean and coffee varieties. They demonstrate that selecting shade-tolerant bean cultivars and stable coffee varieties can substantially improve productivity in multi-strata systems, while many common cover crops are unsuitable beneath canopy shade. Rapid phenotypic screening could offer a low-cost strategy for optimizing crop mixtures and guiding breeding efforts toward greater shade resilience in smallholder multi-strata agro-ecosystems.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1770976</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1770976</link>
        <title><![CDATA[Macro-scale risk assessment and host response to orange rust (Puccinia kuehnii) in sugarcane across tropical zones under current and future climate scenarios]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Melissa Montoya-Arbeláez</author><author>Juan Carlos Ángel-Sánchez</author><author>Mauricio Salazar-Yepes</author><author>Soraida Rojas-Vargas</author><author>Freddy Fernando Garcés-Obando</author><author>Joaquín Guillermo Ramírez-Gil</author>
        <description><![CDATA[Puccinia kuehnii, the causal agent of orange rust, is an obligate biotrophic fungus and an emerging phytosanitary threat to sugarcane production. Although widely distributed in Colombia, its current impact and future risk under climate change remain poorly understood. This study evaluated the present and potential future risk of orange rust in sugarcane systems dedicated to sugar and panela production in Colombia. A total of 252 disease occurrence records were collected from nine sugarcane-producing departments, and disease impact was assessed in commercial fields using a damage index across different sugarcane varieties. Spatial kernel analyses were used to characterize the distribution of disease damage, and the Kruskal-Wallis test was applied to compare severity among varieties. In parallel, ecological niche modeling was used to estimate the current and future potential distribution of P. kuehnii under climate change scenarios. Orange rust was detected in all sampled regions, with reaction scores above 5 and severity values reaching 50%. The highest disease intensity was recorded in Santander and Caquetá, particularly in varieties RD 75-11 and CC 93-7510, while variety CC 01-1940 also showed marked severity in the Cauca River Valley. The ecological niche models identified areas of high current environmental suitability and projected an expansion of favorable conditions into additional sugarcane-producing regions under future climate scenarios. Our findings show that orange rust poses a substantial current and future risk to Colombian sugarcane systems and provide a multiscale spatial framework to support surveillance, varietal management, and evidence-based decision-making for the sugarcane sector.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1765811</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1765811</link>
        <title><![CDATA[Cont-Transformer: enhancing image recognition for orchard fruit-boring pest monitoring with contrastive learning]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jingjun Cao</author><author>Xiaoqing Xian</author><author>Zhida Duan</author><author>Wanxue Liu</author><author>Guifen Zhang</author><author>Lihua Jiang</author>
        <description><![CDATA[The codling moth (Cydia pomonella) is the most serious pest of apple and pear orchards worldwide and has been designated as a quarantine or regulated pest by over 20 countries or regions globally. Morphological identification of the codling moth is highly specialized and time-consuming. In apple and pear production process, codling moth usually coexists with other insect species, including Carposina niponensis, Dichocrocis punctiferalis, Euzophera pyriella, Grapholita molesta, and Helicoverpa armigera, and sometimes the adults of Cydia trasias and Plutella xylostella; these moth species are morphologically similar to codling moth, especially in the pre-adult stages (e.g., egg, larva, and pupa) of them. This study provides an effective solution for distinguishing the codling moth from other insect species with similar appearances automatically; a new Transformer-based model, known as Cont-Transformer, is proposed. Specifically, contrastive learning is introduced to improve distinguishing ability, which contributes to minimizing the similarity of classification labels corresponding to different labels and maximizing the similarity of classification labels of samples with the same label. The cross-entropy loss and contrastive loss are combined to guide the model in focusing on the most discriminative regions. Furthermore, we systematically analyzed various data augmentation strategies to bolster the model’s robustness and generalization, including AutoAugment, RandAugment, TrivialAugment, MixUp, and CutMix. We evaluated the proposed model architecture Cont-Transformer through comprehensive model training and testing on an insect image dataset containing 26 insect categories and a total of 14,431 images. The proposed recognition model achieved accuracy, precision, and recall rates of 99.45%, 99.40%, and 99.45%, respectively, outperforming eight other popular models, i.e., AlexNet, ResNet-50, DenseNet-121, ShuffleNet-v2, EfficientNet-b0, DeiT, MobileViT, and Swin Transformer. Moreover, the developed codling moth investigation program can identify various stages, including egg, larva, pupa, and adult, of these moth species. The present findings should be significant for precise pest control and quarantine supervision.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1797361</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1797361</link>
        <title><![CDATA[Integrating phenological time-series and machine learning for remote sensing crop classification: a case study in the Hetao Plain, China]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yincong Xing</author><author>Yanzhong Li</author><author>Kang Liang</author><author>Jing Tian</author><author>Peng Bai</author>
        <description><![CDATA[To address the challenges of crop classification in China’s smallholder farming systems—characterized by complex cropping patterns and high spectral similarity—this study proposes a classification method based on remote sensing technology combined with phenological monitoring. Using the Google Earth Engine (GEE) platform, the method integrates long-term Landsat satellite imagery with phenological feature analysis. Initial training samples are extracted using NDVI time-series decision rules, followed by spatial filtering to enhance sample quality. A random forest model is then applied to achieve high-accuracy crop classification. Taking the Hetao Plain as a case study, we generated 30-meter resolution crop classification maps (wheat, maize, sunflower, and vegetables) for the period 2000–2024. Validation with field survey points shows an overall accuracy exceeding 90% and a Kappa coefficient greater than 0.88, confirming the approach’s effectiveness. Spatiotemporal analysis reveals a shift in cropping structure toward higher-value crops over the past 25 year, with a significant decline in wheat acreage and continuous expansion of maize and sunflower cultivation. By effectively integrating phenological time-series information with machine learning, this study provides a robust solution for crop classification in complex agricultural landscapes, supporting sustainable agricultural and water resource management.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1752201</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1752201</link>
        <title><![CDATA[Potential use of treated wastewater for sustainable management of durum wheat under water-scarce environments]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Noemi Tortorici</author><author>Antonio Giovino</author><author>Carmelo Mosca</author><author>Mauro Sarno</author><author>Teresa Tuttolomondo</author><author>Nicolò Iacuzzi</author>
        <description><![CDATA[Water scarcity increasingly threatens agricultural sustainability, particularly in arid and semi-arid regions. Treated wastewater (TWW) represents a promising non-conventional water resource for irrigation, offering economic and environmental benefits while contributing to freshwater conservation. Despite concerns over anthropogenic contaminants, its physiological effects on key crops such as durum wheat remain underexplored. In this study, durum wheat (Triticum turgidum L. var. durum) was irrigated with TWW or freshwater (FW) and subjected to four levels of water stress (100%, 70%, 50%, and 30% of full irrigation), with or without the addition of a microbial consortium, to assess growth, physiological traits, and stress adaptation mechanisms. Overall, TWW produced vegetative, physiological, and productive performances comparable to or slightly higher than FW, without evidence of phytotoxicity. The microbial consortium showed variable effects, including occasional negative interactions under severe water deficit, highlighting the importance of soil–plant–microbe interactions and local pedo-climatic conditions. Controlled water stress reduced yield even at moderate levels, although gas exchange data indicate that moderate deficit irrigation (70%) could be physiologically tolerated, but did not translate into higher yield under pot conditions. These findings support the potential use of TWW for durum wheat cultivation under water-limited conditions and provide new insights into plant physiological responses under combined irrigation and microbial treatments. Further studies should evaluate these effects across multiple seasons and in open-field conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1781888</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1781888</link>
        <title><![CDATA[Integration of mulch and liquid fertilizer improves productivity and quality of strawberries in the north-western Himalayas, India]]></title>
        <pubdate>2026-04-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Avinash Chandra Rathore</author><author>Anand Kumar Gupta</author><author>Harsh Mehta</author><author>Charan Singh</author><author>Pawan Kumar</author><author>J. Jayaprakash</author><author>Lekh Chand</author><author>Dinesh Jinger</author><author>Tanushree Sahoo</author><author>Shreya Nivesh</author><author>R. B. Meena</author><author>Vijay Kumar Doharey</author><author>M. Muruganandam</author><author>M. Madhu</author>
        <description><![CDATA[India produces approximately 19.84 thousand metric tons of strawberries from 3.03 thousand hectares, but this amount needs to be increased to meet the growing demand. The USA (65.0 t ha−1) and India (6.55 t ha−1) exhibit very different levels of strawberry productivity. The average global productivity, however, is 23.37 t ha−1, which can be improved through the integration of multiple technologies, such as mulching, farmyard manures, and liquid fertilizers containing macro- and micronutrients. Therefore, the present study was conducted on strawberries (cv. Chandler, Camarosa, and Winter Dawn) with varying mulch and liquid fertilizer levels during 2019–2022 to improve strawberry productivity and quality. The experiment included 36 treatments (fertilizers with four levels, mulches with three levels, and cultivars with three levels), replicated three times and arranged in a split–split plot design (SSPD). The results demonstrated positive correlations among most traits under investigation, except for acidity, which was negatively correlated with fruit yield. Principal component analysis revealed a total variability of 85.5% among genotypes, contributed by PC-1 (77.2%) and PC-2 (8.3%). The Chandler variety with polythene + paddy straw mulch and liquid fertilizer level 1, exhibited the highest levels of vegetative growth, fruit output, fruit quality, and dry biomass. Therefore, to maximize net yield in strawberries, the use of liquid fertilizers supplying macro- and micronutrients in combination with polythene + paddy straw mulch on raised beds proved both effective and profitable.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1737831</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1737831</link>
        <title><![CDATA[Arbuscular mycorrhizal fungi as a promising strategy to enhance root architecture in durum wheat (Triticum durum Desf.) cultivars under salinity stress]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Neda Zavarshani</author><author>Mansour Taghvaei</author><author>Mehdi Zarei</author><author>Beata Dedicova</author>
        <description><![CDATA[Abiotic stress from salinity poses a significant challenge for crop plants, significantly impairing their growth. This study included three experiments: the first focused on evaluating the germination parameters of four durum wheat varieties under various salinity levels. The treatments involved four varieties (Shabrang, Taban, and lines D-98–8 and D-98-10) tested with NaCl solutions at nine salinity levels (0, 2, 4, 6, 8, 12, 16, 18, and 20 dS/m). The second experiment assessed seed cell membrane permeability to differentiate between salt-sensitive and salt-tolerant cultivars. The third examined the role of arbuscular mycorrhizal fungi in enhancing salt tolerance in both cultivars. The same four durum wheat varieties were used in the second experiment. The third experiment involved two durum wheat varieties (the salt-tolerant Shabrang and the salt-sensitive D-98-8), nine NaCl salinity levels, and inoculation with the fungus Funneliformis mosseae. Results from the first experiment showed that salinity stress reduced germination parameters across all cultivars, with a greater decline in the D-98–8 line. Additionally, the seed electrical conductivity test from the second experiment indicated that salinity tolerance in durum wheat was linked to lower cell membrane permeability. The Shabrang cultivar demonstrated the lowest permeability and leakage, while D-98–8 exhibited the highest. Therefore, both experiments identified Shabrang as salinity-tolerant and D-98–8 as salinity-sensitive. The third experiment showed that introducing mycorrhizal fungi increased root phosphate levels and improved root structure under saline conditions, with more pronounced effects in the salinity-tolerant cultivar.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1822224</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1822224</link>
        <title><![CDATA[Correction: Performance of industrial hemp cultivars across U.S. Midwestern environments: evidence from multi-location trials in Missouri]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Sakina Haruna Mahdi</author><author>Joshua Yeboah Asiamah</author><author>Kusum Raj Tamang</author><author>Prabesh Koirala</author><author>Clement Akotsen-Mensah</author><author>Christian B. Carson</author><author>Emily Anne Reed</author><author>Kudiabor Kofi Ntsunyo</author><author>Swastika Sharma</author><author>Shreesha Padyana</author><author>Jaimin S. Patel</author><author>Babu Valliyodan</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1765116</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1765116</link>
        <title><![CDATA[Long-term manurial endowment on soil culturable bacterial dynamics, biological properties, and productivity of cabbage in acid Inceptisols]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Manisha Panigrahy</author><author>Narayan Panda</author><author>Sarmistha Priyadarshini</author><author>Akhilesh Kumar Gupta</author><author>Anshuman Nayak</author><author>Kshitipati Padhan</author><author>Shraddha Mohanty</author><author>Munmun Dash</author><author>Sanjib Kumar Sahoo</author><author>Hrusikesh Patro</author><author>Debadatta Sethi</author>
        <description><![CDATA[IntroductionSoil culturable bacterial population dynamics is crucial for soil health which suffers due to overexploitation and other unsustainable practices. Long-term manurial endowments are becoming more common as a component of regenerative agriculture linked to crop productivity.MethodsThe present study was undertaken to give an insight into regenerative agriculture. A total of 10 manurial practices formulated with integrating soil test-based inorganic fertilizers (STD), farmyard manure (FYM), vermicompost (VC), consortia biofertilizers (BFs), lime (L), organic I, and organic II were evaluated.ResultsThe integrated treatments T8 (STD + VC at 2.5 t ha−1 + BFs + L) significantly enhanced the diversity of beneficial bacterial genera such as Bacillus, Pseudomonas, Rhizobium, Azotobacter, Azospirillum, and Alcaligenes. Biological activities like soil enzyme activities (urease and dehydrogenase), microbial biomass carbon, and nitrogen were synergistically proliferated in T8 (STD + VC at 2.5 t ha−1 + BFs + L). After long-term utilization of STD + VC at 2.5 t ha−1 + BFs + L (T8) MBC, MBN, and DHA were enhanced by 36%, 85%, and 12% over soil test doses of the fertilizer application package. Physiological plant traits, leading to the highest cabbage yield (22 t ha−1), head circumference (42.53 cm), and total chlorophyll content (2.11 mg g−1), demonstrated strong interdependence between microbial health and plant performance. Combining bio-inoculants and liming techniques increased the economic yield of cabbage by 13%-21% at P = 0.05 over control. The antibiotic sensitivity profiling reflected adaptive responses by long-term manurial endowment on soil ecological dynamics. Multivariate analyses, including PCA, regression, and path modelling, confirmed the pivotal role of culturable soil bacterial population dynamics for optimizing both microbial functions and cabbage productivity.ConclusionThe study underscores the importance of integrating inorganics, organics, bio-inoculants, and strategic chemical amendments for a regenerative agriculture in acid Inceptisols.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1764002</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1764002</link>
        <title><![CDATA[Hybrid LSTM-edge correction architecture for physics-informed crop health monitoring in distributed agricultural robotics]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rongchuan Yu</author><author>Yongsheng Xie</author><author>Rifeng Wang</author><author>Wenxin Li</author>
        <description><![CDATA[Agricultural robotics-enabled crop health monitoring faces critical trade-offs: standalone on-device models sacrifice accuracy for real-time responsiveness, while cloud-dependent approaches suffer from high latency and communication overhead. Additionally, data-driven models often lack biophysical plausibility, leading to unreliable predictions for agronomic decision-making under resource constraints. We propose a hybrid LSTM-edge correction architecture that hierarchically integrates lightweight Long Short-Term Memory (LSTM) networks on field robots with physics-informed neural networks (PINNs) at the edge. On-device LSTMs process localized sensor data (soil moisture, spectral reflectance) to generate initial crop stress probability estimates with minimal latency. Edge-based PINNs refine these predictions by embedding biophysical dynamics—modeled via coupled partial differential equations (PDEs) governing the soil-plant-atmosphere continuum (SPAC)—to ensure agronomic validity, mitigate sensor noise, and account for spatial variability. The framework is deployed on NVIDIA Jetson Nano (local inference) and AMD EPYC servers (edge processing), seamlessly integrating with existing farming infrastructures to replace rule-based thresholds with adaptive, physics-grounded control commands. A Fourier Neural Operator (FNO) optimizes the edge PINN’s computational efficiency for high-dimensional PDE solving. Experimental evaluations on two real-world datasets (soybean and citrus) demonstrate that the hybrid approach improves prediction accuracy by 18% compared to standalone LSTMs (F1-score: 0.89±0.02 for soybean, 0.83±0.03 for citrus) while maintaining real-time performance (end-to-end latency: 210 ms, energy consumption: 5.1 J/prediction). Field deployment on a 50-hectare soybean farm yields tangible agronomic benefits: 22% reduction in irrigation water usage, 18% fewer pesticide applications, and 95% system uptime under field conditions. The framework exhibits robust performance against sensor noise (≥80% accuracy at 30% noise-to-signal ratio) and outperforms cloud-based PINNs (72.8% lower energy consumption) and threshold-based methods (28–33% higher F1-score). This work advances distributed agricultural robotics by bridging data-driven machine learning and domain-specific physics, delivering a scalable, interpretable, and resource-efficient solution for precision agriculture. The hierarchical prediction-correction pipeline balances real-time responsiveness with biological plausibility, making it suitable for resource-constrained field robots. By integrating legacy sensors and adaptive actuation control, the architecture offers a practical pathway to upgrade existing farming systems, enabling data-informed interventions while reducing environmental impact.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1739828</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1739828</link>
        <title><![CDATA[Unveiling the legacy of traditional agroforestry systems and their role in enriching soil carbon and nitrogen dynamics in the Chhattisgarh Plain Region]]></title>
        <pubdate>2026-03-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Risikesh Thakur</author><author>Somanath Sarvade</author><author>Atul Kumar Shrivastava</author><author>Surendra Kumar Rai</author><author>Naresh Kumar Bisen</author><author>Shailendra Bhalawe</author><author>Ajay Singh Lodhi</author>
        <description><![CDATA[IntroductionA field study was undertaken during 2021–22 in a three-village cluster located within the Chhattisgarh Plain Agro-Climatic Zone of Madhya Pradesh to evaluate the influence of traditional agroforestry systems on soil organic carbon (SOC) and nitrogen (N) reserves. The study examined how diverse land-use systems contribute to soil quality enhancement through carbon and nitrogen dynamics. However, a clear research gap exists in the region-specific quantification of SOC and nitrogen stocks under traditional, farmer-managed agroforestry systems in this agro-climatic zone. Most earlier studies have focused on mono-cropping or generalized agroforestry models, with limited landscape-level comparisons. The novelty of this study lies in its site-specific, comparative assessment of carbon-nitrogen dynamics across traditional agroforestry systems, generating empirical evidence from a relatively underexplored region and highlighting their role in soil fertility improvement and climate-resilient land management.MethodsEight land-use systems were assessed, namely Agri-silviculture (AS), Agri-horticulture (AH), Agri-horti-silviculture (AHS), Silvi-pasture (SP), Agri-silvi-pasture (ASP), Agri-horti-pasture (AHP), Home gardens (HG), and Cultivated land (CL). Soil samples were collected and analyzed for bulk density (BD), soil organic carbon (SOC), total nitrogen (TN), carbon and nitrogen stocks, and microbial biomass carbon and nitrogen (SMBC and SMBN). Data were subjected to statistical analysis, including one-way ANOVA for significance testing (P ≤ 0.05) and Principal Component Analysis (PCA) to identify major sources of variation among systems.ResultsHome gardens consistently demonstrated superior soil quality compared to other land-use systems, characterized by lower bulk density and markedly higher soil organic carbon (SOC), total nitrogen (TN), carbon and nitrogen stocks, and microbial biomass (SMBC and SMBN). In contrast, cultivated land exhibited greater soil compaction and comparatively reduced carbon, nitrogen, and microbial biomass levels. All soil parameters differed significantly among the land-use systems (P ≤ 0.05), indicating strong land-use influence on soil properties. Principal Component Analysis (PCA) revealed that the first two components together explained 75.71% of the total variation, with PC1 alone accounting for the majority share (65.14%), highlighting that carbon-nitrogen dynamics and microbial attributes were the dominant factors differentiating the systems.ConclusionThe study demonstrates that traditional agroforestry systems, particularly home gardens and silvi-pasture, play a crucial role in enhancing soil organic carbon, nitrogen reserves, and microbial biomass compared to cultivated lands. The presence of perennial vegetation and diverse plant components contributes to improved soil structure, fertility, and biological activity. These results emphasize the ecological and agronomic benefits of integrating agroforestry practices for sustaining soil health and ensuring long-term productivity in the Chhattisgarh Plain Agro-Climatic Zone.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1808128</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1808128</link>
        <title><![CDATA[Correction: Quantifying consumptive water footprints of soybean in rainfed and irrigated systems under climate change scenarios]]></title>
        <pubdate>2026-03-27T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Wilfredo Barrera</author><author>Francesco Morbidini</author><author>Carmelo Maucieri</author><author>Maria Giordano</author><author>Tjaša Pogačar</author><author>Marko Flajšman</author><author>Graziano Ghinassi</author><author>Leonardo Verdi</author><author>Roberto Ferrise</author><author>Anna Dalla Marta</author>
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