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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>
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        <pubDate>2026-05-07T09:53:00.699+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1801099</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1801099</link>
        <title><![CDATA[Weed suppression and reduced fertilizer requirements in organic durum and bread wheat using forage legume living mulches in Mediterranean systems]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Danilo Scordia</author><author>Francesca Calderone</author><author>Aurora Maio</author><author>Tommaso La Malfa</author><author>Marianna Oteri</author><author>Aurelio Scavo</author><author>Fabio Gresta</author>
        <description><![CDATA[Living mulch is a promising strategy for addressing agricultural challenges in organic wheat-based systems. However, success depends on selecting and managing living mulch species able to reduce interspecific competition with the main crop. This study aimed to assess whether forage legume living mulches can improve weed suppression, reduce nitrogen (N) fertilizer requirements, and enhance wheat performance across two growing seasons (2022/23 and 2023/24) in a Mediterranean organic farming system. Two wheat species, Triticum turgidum subsp. durum (var. Core) and Triticum aestivum (Evolutivo population - Evo) were grown individually with forage legume (Trifolium subterraneum or Medicago polymorpha or Lotus corniculatus) and compared with wheat-sole crops under no, medium, and high N fertilization (F-rates). Compared with wheat sole crops, wheat-living mulches slightly decreased or increased grain yield in Core (-3% to +40%) and Evo (-9% to +20%), and maintained or increased the grain protein content (+1 to +10%). The relationship between kernel dry weight and growing degree days showed that wheat sole crops achieved the greatest kernel weight at the highest F-rate. In contrast, the wheat-living mulch systems maintained stable kernel weights under reduced F-rates. Depending on environmental conditions, Trifolium living mulch provided the highest biological N-fixation (21 to 73 kg N ha-1 yr-1), while Medicago living mulch was the most effective in suppressing weed dry biomass (-58 to -94%) as compared with both Core and Evo sole crops. These findings indicate that forage legumes can be safely integrated as living mulches in organic wheat without compromising yield, while improving weed control and reducing dependence on external N, particularly under low nutrient and low-yielding conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1822170</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1822170</link>
        <title><![CDATA[Identifying priority areas for wind erosion prevention through GIS-based suitability analysis]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lenka Lackóová</author><author>Mária Tárníková</author>
        <description><![CDATA[Wind erosion is projected to intensify under climate change due to increasing drought frequency, reduced soil moisture, and declining vegetation cover, particularly in sandy lowland agroecosystems. This study aims to develop a standardized GIS-based suitability modelling framework for identifying priority areas for wind erosion prevention and to evaluate its consistency with a process-based soil loss model. The analysis was conducted in a ~2,000 km² region of western Slovakia (districts of Malacky, Senica, and Skalica), characterized by sandy soils and high erosion susceptibility. A multi-criteria evaluation approach integrating soil erodibility, field length, vegetation cover, and topographic exposure was implemented using binary and weighted overlay methods, with weights derived through the Analytic Hierarchy Process (CR = 0.017). Model outputs were compared with wind erosion estimates based on the Woodruff–Siddoway equation. The results show clear differences among modelling approaches. The binary model identified only 0.62 km² of highly vulnerable land, representing extreme hotspots, whereas the weighted overlay approach delineated 3.88 km², capturing a broader gradient of susceptibility. The process-based model estimated the largest affected area (4.34 km²), reflecting maximum erosion potential. Spatial comparison revealed a consistent increase in extent (binary < weighted < soil loss model). Statistical analysis confirmed a significant moderate correlation between weighted suitability and soil loss estimates (Pearson r = 0.48, p = 0.0013; Spearman r = 0.49, p < 0.001; n = 104). These findings indicate that while process-based models quantify erosion potential, suitability modelling provides more actionable spatial prioritization for preventive measures such as shelterbelts and vegetation management. The proposed framework is transferable and data-efficient, supporting climate adaptation and soil conservation planning.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1706873</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1706873</link>
        <title><![CDATA[Enhancing weed detection in wheat with hybrid attention mechanism CBAM: a comparative study with deep learning approaches]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author> Ritika</author><author>Savita Kumari Sheoran</author><author>Arjun Singh</author><author>Dilip Kumar</author>
        <description><![CDATA[IntroductionWeed infestation is a major constraint on wheat growing, yet traditional detection methods are hampered by the close visual similarity between weeds and crops under field conditions. Although convolutional neural networks (CNN) have a strong potential for automated weed detection, their efficacy remains limited by challenges in feature selection and computational demands. Existing attention-based approaches apply channel and spatial attention in sequence, creating inter-relationship dependencies that limit the quality of the representation of features. This study addresses this gap by proposing a parallel configuration of the CBAM (Convolution Block Attention Module) for the classification of wheat and weed.MethodsA dataset of 1 260 field images (784 wheat; 476 grass) was collected in the village of Berli Khurd in Rewari, Haryana, India, under different lighting conditions and supplemented to 6634 images to resolve the class imbalance and improve the overall coverage. Four widely accepted CNN architectures - ResNet-50, ResNet-152, VGG-16 and VGG-19 - and their variants enhanced by CBAM have been systematically assessed for the classification of wheat and weed. The proposed parallel CBAM configuration process will simultaneously focus both the channel and the spatial attention on the same input feature map, allowing independent and complementary refinement of the features. Models were trained for 30 epochs with AdamW optimization (learning rate = 0.0001) and a batch size of 16 at Google TPU (V2-8) runtime.ResultsCBAM integration significantly improved classification performance across all architectures. CBAM-ResNet-50 and CBAM-ResNet-152 both achieved near-perfect validation accuracy of 99.5%, compared to 89.2% and 98.0% for their baseline counterparts representing improvements of 10.3% and 1.5%, respectively. CBAM-ResNet-50 attained precision of 99.1%, recall of 98.5%, and an F1-score of 99.2%; CBAM-ResNet-152 achieved precision of 99.4%, recall of 98.6%, and an F1-score of 99.3%. VGG-16 and VGG-19 also improved substantially, from 87.0% to 97.3% and from 88.2% to 98.4%, respectively.ConclusionThe parallel configuration of the CBAM consistently improved the robustness of the extraction of features, reduced misclassification and improved coverage across all four CNN architectures. Attention-based gains were largest in the residual networks, indicating that skipping the link increases the benefits of parallel hybrid attention. These findings demonstrate the practical value of targeted residual networks for reliable and field-based weed identification in precision agriculture.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1788673</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1788673</link>
        <title><![CDATA[Assessing the sustainability of field crop farms in the European Union through a multidimensional approach based on composite indicators]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gabriela Ignat</author><author>Lilia Șargu</author><author>Ciprian Ionel Alecu</author><author>Ioan Prigoreanu</author><author>Carmen-Elena Coca</author><author>Nicu Șargu</author><author>Olga Timofei</author>
        <description><![CDATA[IntroductionThe study was conducted to respond to the growing need for standardized and comparable tools for assessing the sustainability of farms in the European Union, given that the green transition, the reform of the Common Agricultural Policy, and the objectives of the 2030 Agenda require the simultaneous measurement of economic, social, environmental, and institutional performance. The main research question was whether the theoretical structure of sustainability based on four pillars is empirically supported and whether these pillars can be integrated into a robust synthetic index. The hypothesis tested assumed the existence of distinct latent dimensions for each pillar and persistent territorial disparities within the EU.MethodsThe analysis used the FADN/FSDN database for the period 2010–2023, including farms specializing in field crops from all European Union Member States. Economic, social, environmental, and institutional indicators aligned with international reference frameworks were selected, and the values were standardized using z-scores. A Farm Sustainable Development Index (FSDI) was constructed for each pillar, and their integration generated the Farm Sustainability Index (FSI). Internal consistency was assessed using Cronbach’s α coefficient, and structural validity was assessed using PCA analyses applied both individually to the pillars and globally, with factor extraction and varimax rotation.ResultsThe results support the unidimensionality hypothesis for the economic, environmental, and CAP pillars, which show high levels of internal consistency, while the social pillar shows moderate but acceptable consistency. The overall PCA validates the theoretical four-pillar structure, showing clear factor groupings. The FSI index highlights a persistent polarization between high-performing Nordic and Western European countries, an intermediate group with moderate sustainability, and a cluster of Southern and Eastern countries with negative values. Temporally, the results indicate stagnation between 2010 and 2016, followed by an improvement after 2020, associated with CAP reforms and the green transition.DiscussionsThe results suggest that agricultural sustainability in the European Union is structured on a stable territorial model, which has important implications for public policy design, CAP fund allocation, and the implementation of green transition strategies. The limited convergence among Member States highlights the need for differentiated interventions tailored to regional contexts. The findings are consistent with recent literature on economic and ecological polarization in agriculture, and the proposed FSI index can be used as a robust tool for monitoring performance and evaluating the effects of future policies. Further research should extend the methodology to the regional level (NUTS 2) and integrate additional indicators on climate resilience and farm digitalization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1826804</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1826804</link>
        <title><![CDATA[Comparative analysis of yield formation and grain quality of Japanese rice cultivars under upland cultivations in Hawai‘i and Japan]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sotaro Honda</author><author>Naomi Horiuchi</author><author>Akane Taki</author><author>Kazuo Tanaka</author><author>Megumi Yamashita</author><author>Makoto Yoshida</author><author>Taiichiro Ookawa</author><author>Jonathan L. Deenik</author><author>Tomoaki Miura</author><author>Akinori Yamanaka</author><author>Shunsuke Adachi</author>
        <description><![CDATA[Interest in local food production in Hawai‘i is increasing to address food insecurity. Rice is a promising candidate crop; however, no commercial rice cultivation exists in the state, and quantitative evaluations of yield formation and grain quality under Hawai‘i conditions are lacking. Here, we evaluated the agronomic performance of two elite Japanese rice cultivars, Koshihikari and Hitomebore, grown under direct-seeded upland conditions in both Hawai‘i and Japan in the 2025 summer growing season. The grain yield of Koshihikari and Hitomebore in Hawai‘i reached 3.79–4.01 t ha−1, which was comparable to the yields obtained in Japan (3.89–4.16 t ha−1), despite a markedly shorter growth duration. Although the spikelet number per unit area was lower in Hawai‘i, this reduction was compensated for by a higher grain-filling rate and greater thousand-grain weight. In addition, rice produced in Hawai‘i exhibited a higher whole grain ratio and a lower incidence of white immature grains compared with rice grown in Japan. These advantages were associated with higher solar radiation and moderate temperatures during the post-heading period. These findings demonstrate the agronomic feasibility of cultivating Japanese japonica rice cultivars under upland conditions in Hawai‘i.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1831688</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1831688</link>
        <title><![CDATA[Bioherbicides and agroecology: challenges and opportunities for agroecological weed management]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Milos Zaric</author><author>Vesna Dragičević</author><author>Milena Simić</author><author>Natalija Pavlović</author><author>Milan Brankov</author>
        <description><![CDATA[The limited availability of herbicides for weed management, coupled with the rapid expansion of herbicide-resistant weed populations, has intensified the need to explore alternative weed management strategies, particularly for producers transitioning to and/or operating in organic farming systems. The expansion of bioherbicides in the US market has opened new opportunities in weed control and new research avenues for weed management. Bioherbicides are natural substances with herbicidal activity, and are mostly non-selective and do not translocate within plants. They are directly connected with agroecological principles by serving as sustainable, nature-derived complementarily to synthetic herbicides aiming to control weeds while preserving biodiversity, soil health, and ecological balance. Their non-selective activity requires special attention because they may also affect crops, while targeted application may be needed for safe use. Because some bioherbicides are new to the market, data on their effectiveness against troublesome weeds is limited. Therefore, bioherbicides, when integrated with precision application technologies and non-chemical tactics, may serve as a bridging strategy for systems facing herbicide resistance. This perspective discusses the challenges and opportunities of bioherbicides as a new tool for weed control, with particular attention to application technology, regulatory pathways, and ecosystem services.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1790242</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1790242</link>
        <title><![CDATA[Ecosystem-based adaptation practices for smallholder farmers’ climate resilience: perceived effectiveness and co-benefit importance in Mabalane District-Mozambique]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Claudius Patrick Waran</author><author>Jaime Carlos Macuácua</author><author>Nicia Giva</author>
        <description><![CDATA[Ecosystem-based adaptation practices among smallholder farmers in drought-prone areas provides a positive response strategy to the increasing challenges posed by climate change, particularly declining crop yield under dry spells condition. Despite increasing acknowledgement of the role ecosystem-based adaptation practices play in enhancing agricultural resilience, very little studies have been done on the perception of smallholder farmers of the effectiveness of these practices. The objective of this study was to assess the perceived effectiveness of ecosystem-based adaptation practices adopted by smallholder farmers and the co-benefits importance in enhancing climate resilience. The study used a mixed-method approach involving a one-time household survey conducted from 11 September to 11 October 2025, lasting one month, with 360 smallholder farmers targeting household heads in Mabalane district in centre of Gaza Province of Mozambique. Purposive sampling, focus group discussions and key informants’ interviews were used for data collection. The results of the study revealed three key ecosystem-based adaptation practices namely mixed cropping (83.9%), integrated crop-livestock management (57.2%) and mulch-tillage (51.1%) as the most widely adapted practices among smallholder farmers. Perceived effectiveness was highest for practices that visibly improved soil fertility and quality, crop productivity and food security. Furthermore, the study revealed that smallholder farmers prioritize soil fertility improvement, increased crop productivity, enhanced soil moisture retention, and food security improvement. It is concluded that the predominance of the key ecosystem-based adaptation practices in the study area is attributed to their perceived effectiveness and their direct and synergistic contributions to climate resilience within the community’s traditional farming system, and in the context of locally important staple food crops.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1795832</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1795832</link>
        <title><![CDATA[Quantification and mapping of maize crop evapotranspiration using the surface energy balance algorithm for land model and medium-resolution satellite data]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gourav Sabharwal</author><author>Kandasamy Vaiyapuri</author><author>Selvaraj Selvakumar</author><author>Ramasamy Jagadeeswaran</author><author>Marimuthu Raju</author><author>Sellaperumal Pazhanivelan</author><author>Pavithran Periyasamy</author><author>Sakthivel Sivakumar</author>
        <description><![CDATA[Evapotranspiration (ET) is a key variable in the hydrologic cycle that directly affects the redistribution of precipitation and surface balance. ET measurements with high temporal resolution are required for coupling with models of highly dynamic processes, e.g., hydrological and land surface processes. The Amaravathi River Basin is the focus of Tamil Nadu, as its major water resource for industry and major grain producing regions. However, this area is facing serious water resource shortages and water pollution problems. The present study used Landsat 8 satellite remote sensing data, in situ meteorological observations, and the surface energy balance algorithm for land (SEBAL) model with daily vegetative indices and energy fluxes in the River Basin. Moreover, factors influencing ET were also identified based on the results of this study. ET varies with land cover type and physical and chemical properties of the underlying surface. Furthermore, ET is also controlled by water availability, radiation, and other atmospheric weather factors conditions, whereas the CROPWAT model, based on the FAO 56 Penman-Monteith method, was employed to estimate seasonal water requirements and comparison was done with the SEBAL model. The root mean square error (RMSE) for ETa exhibited an RMSE of 0.65 mmd−1 and demonstrated an R2 exceeding 0.93. Finally, based upon crop cutting experiments (CCE), yield data and SEBAL-derived ET spatial water productivity maps were obtained by mathematical operations of the yield and ETa raster maps. The average maize yield of the study area was 7,567 kg ha−1, which was close to official agricultural marketing department data. The seasonal maize crop water requirement based upon SEBAL was 470 mm and that based upon CROPWAT was 506.3 mm, indicating that the SEBAL model shows good capability to detect CWR demand under study at the Amaravathi River basin scale. The average physical and economic maize water productivity was 1.61 kg m−3 and Rs. 40 m−3 across the Amaravathi River Basin, respectively. This study identifies high-performing and low-performing water productivity regions across the Amaravathi River Basin.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1798511</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1798511</link>
        <title><![CDATA[Evaluation of maize (Zea mays L.) genotypes for yield and yield components and carbon storage]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maltase Mutanda</author><author>Sandiswa Figlan</author><author>Asande Ngidi</author><author>Seltene Abady Tesfamariam</author><author>Vincent Chaplot</author><author>Hussein Shimelis</author>
        <description><![CDATA[IntroductionMaize is a global commodity crop cultivated under monocropping systems and diverse environmental conditions. While different genotypes exist with various abilities to store carbon into tissues and into the soil to mitigate against climate change and soil degradation, the links with grain yield remains uncertain.MethodsTherefore, the present study aimed to screen a series of maize germplasm planted in southern Africa for carbon storage and agronomic performance to assess the link between these and identify superior genotypes. We evaluated as a first attempt, forty-five genotypes using a 5 x 9 alpha lattice design across two South African sites during the 2022/23 growing season. The recorded agronomic traits include plant height (PH), grain yield (GY), total plant biomass (PB), shoot biomass (SB) and root biomass (RB) together with total plant carbon stocks (PCs), shoot carbon stock (SCs), root carbon stock (RCs), root to shoot carbon stock ratio (RCs/SCs), and grain carbon stock (GCs).ResultsSignificant (p< 0.05) genetic variations were recorded for all the assessed agronomic traits. The high yielding maize genotypes were TZECOMP3DT-C2, TZE(OMP3DT/WHITEDTSTRSYN)CZ and DT-STR-Y-SYN14. The maize genotypes which sequestered more carbon were TZE(OMP3DT/WHITEDTSTRSYN)CZ, 9022–13 and ZDIPLOBC4-C3-W. High phenotypic and genotypic coefficient of variations were recorded for PB and GY, respectively. The GY showed significant positive correlation with PH, SB, RB and PB. The principal component analysis highlighted that RCs, RCs/SCs, PCs, SCs, and GCs as key contributors to carbon sequestration.DiscussionGenotypes TZECOMP3DT-C2, TZE(OMP3DT/WHITEDTSTRSYN)CZ, TZECOMP5C7/ TZECOMP39TCZ and 9022–13 were selected for their high GY production and carbon storage capacity. The selected genotypes are recommended for production and future breeding.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1814520</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1814520</link>
        <title><![CDATA[Combination of nitrogen reduction and algae-based biostimulants: a sustainable strategy for processing tomato]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maria Eleonora Pelosi</author><author>Ida Di Mola</author><author>Lucia Ottaiano</author><author>Eugenio Cozzolino</author><author>Mohamed Houssemeddine Sellami</author><author>Mauro Mori</author>
        <description><![CDATA[Processing tomato (Solanum lycopersicum L.) is a key crop in the Mediterranean, where farmers are increasingly required to balance high yields with reduced inputs. In response to the EU Farm to Fork strategy, which targets a 20% reduction in fertilizer use by 2030, the aim of this study was to evaluate the potential of algae-based biostimulants to sustain crop performance under reduced nitrogen (N) supply. In this context, we carried out a two-year field trial (2023–2024) in Acerra, Southern Italy testing three N levels (100%, 90% and 80% of the optimal dose in 2023; 100%, 80% and 70% in 2024) and two commercial formulations (BIO1: Auximar + Procalcium; BIO2: Enerleaf + Pentacalcium), applied as foliar sprays. Reducing N dose led to a proportional decline in yield compared to the optimal dose (–16% in 2023, mean value of N80% and N90% and –30% in 2024 with N70%). In both years, all biostimulants increased marketable yield (+21% and +36%), mainly by raising fruit number. The N80% treatment combined with biostimulants maintained yields comparable to the full nitrogen dose, pointing to a more efficient use of fertilizer. Biostimulant treatments enhanced fruit firmness (+19% in 2023) and soluble solids total (+14% and +10% in 2023 and 2024, respectively), and boosted nutritional parameters such as ascorbic acid (12% in 2023 and 15% in 2024) and carotenoids. Lycopene was not influenced by biostimulants, but showed instead a strong dependence on seasonal conditions and N dose, with higher values recorded in 2024. Although main effects of nitrogen and biostimulants were largely independent, significant year × nitrogen × biostimulant interactions were detected for fruit nitrogen and nutraceutical compounds. The results indicate that algae-based products with a moderate (≥20%) N reduction appears to be a practical way to cut fertilizer inputs while safeguarding both yield and fruit quality in processing tomato.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1817915</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1817915</link>
        <title><![CDATA[Can high resolution UAV-derived weed maps replace camera-based targeted spray application?]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Madhusudhan Adhikari</author><author>Sudhir Payare</author><author>Ricardo Pinto</author>
        <description><![CDATA[Efficient weed mapping is critical for enabling site-specific weed management (SSWM) and ensuring effective targeted herbicide application in no-till dryland cropping systems. Producers are increasingly interested in using UAV (Unmanned Aerial Vehicle) based weed mapping to generate prescription maps as a lower-cost alternative to camera-based systems, yet the accuracy and operational feasibility of these UAV-derived methods remain uncertain. This study compared three UAV-based weed detection approaches, RGB Excess Green (ExG) index, NIR band thresholding, and a YOLOv11 deep learning model against a ground-based camera reference system across three mapping dates in a summer fallow field. UAV imagery was collected using RGB and multispectral sensors, and YOLO was trained on annotated RGB image tiles. Key performance metrics evaluated were prediction accuracy (MAE, RMSE, and R2), omission rates (%) and Bland-Altman analysis (bias and Limits of Agreement-LoA). Results showed that all UAV methods systematically underpredicted weeds compared to camera-based, with bias and variability increasing with weed density and from early season (April 24) to late season (June 24). RGB consistently provided the lowest errors and most stable agreement, YOLO tracked spatial patterns but had the largest underestimation, and NIR performance varied according to soil and residue conditions. Weed omission rates ranged from 41–65% for RGB, 48–73.5% for NIR, and 78–85% for YOLO. These findings demonstrate that the effectiveness of UAV-based maps is constrained by image spatial resolution, which can limit the detection of small weeds that are more vulnerable to management practices. In contrast, real-time camera-based systems improve the detection of small weeds at application, increasing herbicide application efficiency in large-scale operations. Future research should focus on refining UAV image acquisition protocols and integrating advanced sensing technologies to improve weed detection reliability across the growing season and support more effective site-specific weed management.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1795286</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1795286</link>
        <title><![CDATA[Sowing methods and early-season weed interference duration as key drivers of yield loss reduction in canola]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhammad Awais Arshad</author><author>Rana Nadeem Abbas</author><author>Usman Zulfiqar</author><author>Ali Ahmad</author><author>Go’zal Burxonova</author><author>Mayank Anand Gururani</author>
        <description><![CDATA[Canola is a major oilseed crop of semiarid regions throughout the world. The productive performance of canola is influenced by different sowing methods and durations of weed–crop interference. Appropriate sowing methods combined with timely weed management during the early growth stages can significantly improve canola productivity. A field experiment was conducted to evaluate the effect of different sowing methods and durations of early-season weed interference on growth and yield components of canola crop at the Agronomy Research Area, Department of Agronomy, University of Agriculture, Faisalabad, during 2021–2022. The experiment consisted of three different sowing methods (flat, bed, and ridge) with five different weed–crop competition periods [weed-free control, weedy up to 30, 40, and 50 days after sowing (DAS), and full-season weed competition]. The design of the experiment was a randomized complete block design (RCBD) with a split-plot arrangement. Each treatment was replicated three times. The results showed that the lowest weed density and fresh and dry weights of weeds were observed in ridge sowing, followed by bed sowing, while the highest were recorded under the flat sowing method. Results for leaf area index, grain yield, biological yield, number of pods, number of grains/pod, harvest index, number of branches, plant height, 1,000-grain weight, and oil content showed the highest values in ridge sowing followed by bed sowing, while the lowest values were recorded under the flat sowing method. Among weed–crop competition periods, the weed-free control (season-long weed removal) produced the highest yield and quality parameters, whereas full-season weed competition resulted in the lowest crop performance. Yield and related traits progressively declined as the duration of weed competition increased from 30 days after sowing. In conclusion, the results showed that sowing method significantly influences canola yield and quality, and prolonged early-season weed interference substantially reduces crop productivity. Based on the results of this study, the ridge sowing method combined with weed-crop competition up to 30 DAS is recommended for canola cultivation.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1724816</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1724816</link>
        <title><![CDATA[The effects of long-term fertilization on rice yields in the red soil region, China: a field meta-analysis]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xian Tang</author><author>Xiaolin He</author><author>Dao Liu</author><author>Yitian Liu</author><author>Feng Li</author><author>Shuai Lan</author><author>Zhangjie Qin</author><author>Xiulong Chen</author><author>Jianrong Zhao</author><author>Feng Liang</author>
        <description><![CDATA[BackgroundRice is the primary grain crop produced in red paddy soil. However, the effects of different fertilization treatments, inherent soil productivity on rice yield and also the factors that influence the rice yield and inherent soil productivity in this region remain unclear.MethodsWe here selected 196 cultivated land quality monitoring sites to assess the effects of no fertilizer (CK), chemical fertilizer plus straw return (NPKS) and chemical fertilizer plus manure (NPKM) on rice yield. We also evaluated the soil properties on the rice yield, basic soil productivity contribution rate to rice yield (BSPCR) and fertilizer contribution rate to yield (FCR); furthermore, we assessed the impacts of specific soil properties on BSPCR and FCR.ResultsThe results showed that NPKM and NPKS significantly increased rice grain and straw yield by 82.63% and 83.05%, respectively, compared to CK. NPKM was found to increase yield more than NPKS did. Rice straw and grain yield both increased along with increases in nitrogen fertilizer application and the available nitrogen (AN) content of the soil. In the NPKM and NPKS plots, higher pH levels were associated with a gradual decrease in the rate of yield increase. Compared with CK plots, increased soil organic matter (SOM), available phosphorus (AP), and available potassium (AK) contents in NPKM and NPKS plots were each associated with initial increases in rice grain and straw yield. Soil pH and SOM contents were determined to be the main factors affecting the BSPCR and FCR.ConclusionThis study indicated that rice yield benefited more from the application of manure than of returned straw. In the future, improving the acidification and SOM contents of red soil paddy would be the most important measures to improve basic soil fertility, the fertilizer utilization rate, and also the rice yield.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1749285</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1749285</link>
        <title><![CDATA[Crop modeling and multivariate analysis of some bread wheat genotypes under drought stress]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Naheif E. Mohamed</author><author>Abdel-rahman A. Mustafa</author><author>Mohamed S. Shokr</author><author>Hala A. M. El-sayed</author><author>Nazih Y. Rebouh</author>
        <description><![CDATA[One important abiotic factor affecting wheat yield and endangering global food security is drought stress. Thus, to evaluate these genotypes based on their water usage efficiency and drought susceptibility index, the current study used 10 extremely diverse bread wheat genotypes over the winter seasons of 2023/2024 and 2024/2025. Additionally, the correlation coefficients between grain yield and its components are broken down into direct and indirect impacts, and the fitted model for grain yield and its components in both normal and drought-stressed environments was evaluated. For every variable examined, there were very significant differences between the genotypes under both favorable and stressful conditions. The genotypes G3, G6, and G8, which are distinguished by a high yield index (YI: >1), low drought susceptibility index (<1), and high yielding stability index, were more adaptive in both environments. While genotypes G2 and G9 were suited to normal conditions in both seasons, genotypes G4 and G7 were adapted to drought stress. Grain yield was directly positively impacted by the characteristics of number of tillers (NT), thousand kernel weight (TKW), and harvest index (HI). Under drought stress, spike length (SL) via TKW and HI, NT via number of spikes (NS) and HI, and TKW via HI had the greatest beneficial indirect effects. The fitted models of grain yield and its components under drought stress comprised biological yield (BYLD), NT, NS, HI, and plant height (PH), TKW, BYLD, HI, which contributed more to grain yield in the first and second seasons at 91.5% and 96.7%, respectively. According to the graphical genotype and environmental interaction (GGE-bioplot) focused scaling for comparison, G3 was the best and most promising genotype under these circumstances, followed by G6, G8, and G4. As a result, these genotypes are thought to be highly advantageous in wheat breeding studies.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1805940</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1805940</link>
        <title><![CDATA[Evaluation of UV-C light for control of Tetranychus urticae and Eotetranychus lewisi (Acari: Tetranychidae) on strawberry]]></title>
        <pubdate>2026-04-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Colin T. Koubek</author><author>Mohammad Amir Aghaee</author>
        <description><![CDATA[Twospotted spider mite, Tetranychus urticae Koch, is a major arthropod pest of strawberry production in California, while Lewis mite, Eotetranychus lewisi McGregor, has emerged as an occasional but increasingly problematic pest in recent decades. Management of both species relies heavily on miticides and biological control, but resistance to miticides in T. urticae and limited biological control options for E. lewisi highlight the need for alternative management tools. Ultraviolet-C (UV-C) light is currently used in commercial strawberry production to manage powdery mildew, yet its efficacy against mite pests under California field conditions is not clear. This study evaluated the ovicidal and lethal effects of UV-C exposure on T. urticae and E. lewisi through laboratory and open-field evaluations, along with dose–response bioassays. Laboratory ovicide assays demonstrated near complete suppression of egg hatch for both species following UV-C exposure at 600, 1200, and 1800 J/m2. Field assays conducted across multiple seasons showed consistent and substantial reductions in egg hatch for T. urticae and E. lewisi, though efficacy varied among trials. Dose–response analyses indicated that larvae were more susceptible than adults, but lethal dose estimates for adults of both species exceeded 9,000 J/m2, a UV-C dose currently not advisable under field conditions. Reported LD50 values confirmed that UV-C is unlikely to provide meaningful direct control of motile life stages at commercially viable exposure levels. To our knowledge, this study is the first to report ovicidal activity and lethal dose estimates for E. lewisi in response to UV-C light. These findings indicate that UV-C applications primarily function as an ovicidal tool for mite pests in strawberry production systems and may complement existing integrated pest management programs by targeting early developmental stages.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1757848</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1757848</link>
        <title><![CDATA[Integrated pest and disease management for banana crops in East and Central Africa: addressing the challenges of co-occurring biotic threats]]></title>
        <pubdate>2026-04-27T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Guy Blomme</author><author>Elizabeth Kearsley</author><author>Walter Ocimati</author>
        <description><![CDATA[Smallholder subsistence farmers dominate banana (Musa spp.) production across East and Central Africa, yet their yields are constrained by a suite of co-occurring biotic threats. Three major diseases, Xanthomonas wilt (XW), Fusarium wilt (FW), and banana bunchy top disease (BBTD) threaten productivity. These are compounded by two established pests (black weevil, plant-parasitic nematodes) and an emerging pest (banana thrips). Integrated pest and disease management (IPDM) protocols aim to mitigate these challenges by coupling disease-specific interventions with broader agronomic practices. While individual IPDM packages share core components, including biosecurity, accurate diagnosis, roguing, clean planting material, resistant cultivars, vector avoidance, and judicious chemical control, their efficacy can be compromised when multiple threats coexist. Synergistic or antagonistic interactions may arise under these conditions. Part 1 of this review synthesizes current literature on the core components of IPDM strategies for these six biotic stresses, highlighting commonalities and divergences among the recommended packages. In Part 2, it further evaluates the role of standard cultural and agronomic practices in disease-pest management outcomes. These include crop diversification, weed management, planting density, sucker and leaf removal, tillage, mineral and organic fertilization, biocontrol, mulching, pest suppression, and irrigation. By identifying compatibilities and incompatibilities in management when biotic constraints co-occur, and by addressing knowledge gaps across the various protocols, the review provides actionable insights for designing holistic extension programs that harmonize prevention and control measures. As such, IPDM programs should be better equipped to avoid unintended trade-offs and enhancing resilience in banana systems where these threats co-occur.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1726240</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1726240</link>
        <title><![CDATA[Surfactant-enhanced fertilizer coating improves nutrient use efficiency and yield in potato]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pooja Mankar</author><author>Sanjay Rawal</author><author>Mayur Kankale</author><author>Sushilkumar Bahuguna</author><author>Jeferson Naue</author><author>Sanjeev Sharma</author><author>Jagdev Sharma</author><author>Rajesh Kumar Singh</author><author>Brajesh Singh</author>
        <description><![CDATA[Global potato production exceeds 380 million tons annually and continues to expand due to rising food demand and growth of the processing sector, increasing pressure on production systems to improve resource-use efficiency while minimizing environmental impacts. In India, where production is expected to increase further, improved nutrient management is essential to sustain productivity under climatic and environmental constraints. Potato production relies heavily on chemical fertilizers, raising concerns about nutrient-use efficiency (NUE) and environmental sustainability. This study evaluated the effects of a non-ionic surfactant soil adjuvant, Silwet Power, combined with varying NPK fertilizer levels on potato growth, tuber yield, nutrient efficiency, and carbon footprint. A 2-year field experiment (2021–2023) was conducted at ICAR–Central Potato Research Institute, Regional Station, Modipuram, India, using a randomized block design with eleven treatments and three replications. Treatments included full, reduced, and zero NPK fertilizer rates applied with or without Silwet Power. Data were analyzed using ANOVA with mean separation at P ≤ 0.05. Application of 75% of the recommended fertilizer dose (270–80–150 kg N–P2O5–K2O ha−1) combined with Silwet Power significantly improved plant growth, tuber yield, and Nutrient Use Efficiency compared to the recommended fertilizer dose alone. This treatment produced a 10.4% higher total tuber yield, enhanced agronomic efficiency and uptake of N, P, and K, increased process-grade tuber yield and size uniformity, and reduced the proportion of oversized and deformed tubers. The yield-scaled carbon footprint was reduced by 11.1%–25.3% relative to the recommended fertilizer dose owing to lower fertilizer input. The results indicate that surfactant-assisted fertilization can enhance fertilizer efficiency and sustain potato productivity under reduced NPK application, offering a promising strategy for sustainable potato production and contributing to climate-resilient nutrient management in intensifying potato-based systems in the future.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1749362</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1749362</link>
        <title><![CDATA[Deep learning-based quantification of tall fescue abundance in pastures]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ross E. Binkley</author><author>Samuel R. Revolinski</author><author>Echo E. Gotsick</author><author>Ray S. Smith</author><author>Zeya Wang</author><author>Katsutoshi Mizuta</author>
        <description><![CDATA[Accurate and rapid measurement of tall fescue abundance is essential for efficient forage management and mitigating livestock exposure to potential endophyte-related toxicity. Traditional visual estimation methods are labor-intensive and are subject to change depending on the observer doing the calculations, which limits their scalability and consistency. This study explores the application of deep learning techniques to automate and improve the classification of tall fescue abundance across pasture systems in Kentucky. Ground-truth abundance classes were evaluated using one of the traditional methods with occupancy grid and compared against estimates derived from red-green-blue images. Two convolutional neural network architectures, YouOnlyLookOnce (YOLO) and ResNet were employed for image-based classification. Among the models tested, YOLOv8x achieved a classification accuracy of 96% across three abundance classes, effectively distinguishing tall fescue from other vegetation and bare soil. Results demonstrated the potential of artificial intelligence in detecting spatial distribution of tall fescue, thereby supporting risk assessment of toxicity. Compared to the traditional methods, the deep learning approach improves efficiency and reduces manual labor efforts while maintaining highly accurate prediction performance for continuous monitoring applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1732106</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1732106</link>
        <title><![CDATA[Methane production potential, soil health and rice yields under inorganic and organic management in the Brahmaputra Valley of Assam, India]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bedabati Kalita</author><author>Leena Borah</author>
        <description><![CDATA[Methane (CH4), a powerful greenhouse gas emitted from flooded rice soils is a major contributor to climate change. Its production is significantly influenced by on-farm nutrient management practices. The present study compared the effects of long-term inorganic and organic management practices on methane production potentials, selected soil health indicators, and grain yields in farmers’ rice fields of Assam. We hypothesized that long-term organic nutrient management increases soil CH4 production potential due to enhanced carbon availability, but simultaneously improves soil health and rice productivity compared to inorganic fertilization. Considering both the years of study, CH4 production potential ranged between ~133 and ~236 CH4 g-1 day-1, organic carbon between 0.38 and 0.70%, microbial biomass carbon between 400 and 547 µg g-1, and grain yield between ~2809 and ~5684 kg ha-1 in the inorganic fields. Similarly, in the organic fields, CH4 production potential, organic carbon, microbial biomass carbon and grain yield ranged between ~167 and 344 CH4 g-1 day-1; 0.50 and 0.91%; 300 and 890 µg g-1; ~5267 and ~7731 kg ha-1 respectively. Comparatively higher CH4 production rates in organic fields may be due to enrichment of the soil organic carbon pool via decomposition of applied organic amendments, leading to enhanced microbial biomass and activity. CH4 production under organic management showed good association with soil pH, organic carbon, and microbial biomass carbon. However, higher CH4 production in organic fields was partly compensated by improved soil properties and rice yields than inorganic fields. Higher grain carbohydrate in organic fields indicate efficient carbohydrate partitioning to the grains and better nutrient utilization by the crop. Vermicompost and vermiwash applied fields recorded lower CH4 production among the organic treatments and higher yield among all the studied treatments. Cow manure and Azolla application also significantly reduced CH4 production but improved yield to a comparatively lesser extent. Our findings suggest that appropriate organic management practices could reduce CH4 production rates in rice soils while enhancing soil health and crop yield.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fagro.2026.1719143</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fagro.2026.1719143</link>
        <title><![CDATA[Response of acidic silt loam soil properties to biochar application under soybean cultivation]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Binaya Baral</author><author>Deepak Khatri</author><author>Sudip Poudel</author><author>Lalit Pun Magar</author><author>Atanu Mukherjee</author><author>Anuj Chiluwal</author>
        <description><![CDATA[Biochar is widely recognized for its potential to stabilize soil acidity and enhance carbon sequestration, yet its functional effects in temperate acidic silt loam soils remain insufficiently quantified. A two-year field study was conducted in Kentucky to evaluate changes in soil physicochemical properties following annual application of 12 t ha-1 of pine (Pinus spp.) sawdust biochar (pyrolyzed at 650 °C) under continuous soybean (Glycine max) cultivation. Composite soil samples (0–15 cm) were collected pre-planting (May) and post-harvest (October) to monitor acidity indices, organic reserves, fertility indices, and hydrological properties. Biochar acted primarily as a chemical and carbonaceous stabilizer rather than an immediate fertility enhancer. The amendment significantly maintained higher post-harvest pH levels compared to the control, effectively buffering against the seasonal acidification observed in unamended plots. Soil organic carbon (SOC) and buffer pH exhibited a distinct “lag effect,” with significant increases emerging only after two years of cumulative application; at the final October 2024 sampling, SOC in the biochar-treated plots had increased to 1.96% from a 1.74% baseline, whereas the control plots declined to 1.66%. Conversely, soil fertility and hydrological parameters showed no significant treatment effects, being driven instead by seasonal environmental factors and crop uptake. These findings suggest that an annual application of 12 t ha−¹ of pine biochar serves as an effective benchmark for mitigating acidification and building organic reserves in temperate silt loams, though further multi-rate studies are required to optimize application dosages for long-term agronomic use.]]></description>
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