The study investigates the effects of seed inoculation with Rizoline-r and foliar application of KALNINI on the growth, physiological parameters, and yield of soybean (Glycine max L.) grown under organic-mimicking conditions. The objective was to enhance nitrogen fixation, photosynthetic efficiency, and yield performance in sustainable farming systems without synthetic inputs. A full-factorial randomized pot experiment was conducted using three soybean varieties (Laulema, Paradis, Tiguan), seed inoculation (3 L t⁻¹ Rizoline-r), and foliar application of 1% KALNINI at the third trifoliate and full flowering stages. The experiment simulated organic production by excluding synthetic fertilisers and pesticides. Results showed that seed inoculation increased plant height by 6.1% and biomass accumulation by 11.0%, while foliar treatment significantly enhanced leaf area index by 28.6% and chlorophyll content by 14.5%. The combined application of both treatments demonstrated a synergistic effect, resulting in the greatest improvements in all traits assessed. Notably, pod formation increased by 34.3%, and seed yield rose by 15.9% compared to the untreated control. These physiological and agronomic enhancements suggest improved plant metabolism, nitrogen uptake, and stress tolerance. The findings underline the potential of integrating microbial inoculants and foliar biostimulants into organic-compatible cropping systems. Such strategies can reduce dependency on synthetic agrochemicals while improving productivity and sustainability. Further research under diverse environmental and soil conditions is recommended to confirm the consistency and adaptability of these results and to develop region-specific treatment protocols suitable for wide adoption in low-input agriculture.

Weeds pose a significant threat to crop yields, both in quantitative and qualitative terms. Modern agriculture relies heavily on herbicides; however, their excessive use can lead to negative environmental impacts. As a result, recent research has increasingly focused on Integrated Weed Management (IWM), which employs multiple complementary strategies to control weeds in a holistic manner. Nevertheless, large-scale adoption of this approach requires a solid understanding of the underlying tactics. This systematic review analyses recent studies (2013–2022) on herbicide alternatives for weed control across major cropping systems in the EU-27 and the UK, providing an overview of current knowledge, the extent to which IWM tactics have been investigated, and the main gaps that help define future research priorities. The review relied on the IWMPRAISE framework, which classifies weed control tactics into five pillars (direct control, field and soil management, cultivar choice and crop establishment, diverse cropping systems, and monitoring and evaluation) and used Scopus as a scientific database. The search yielded a total of 666 entries, and the most represented pillars were Direct Control (193), Diverse Cropping System (183), and Field and Soil Management (172). The type of crop most frequently studied was arable crops (450), and the macro-area where the studies were mostly conducted was Southern Europe (268). The tactics with the highest number of entries were Tillage Type and Cultivation Depth (110), Cover Crops (82), and Biological Control (72), while those with the lowest numbers were Seed Vigor (2) and Sowing Depth (2). Overall, this review identifies research gaps and sets priorities to boost IWM adoption, leading policy and funding to expand sustainable weed management across Europe.

A two-year trial (2021 and 2022) was performed with five different fungicide and foliar fertiliser application strategies to control apple scab (Venturia inaequalis) in integrated apple orchards in Latvia. A strategy of using inorganic fungicides or combining them with synthetic fungicides was compared to a strategy of applications with only synthetic fungicides and untreated control. Furthermore, two strategies included foliar fertilisers to determine whether they may affect apple scab used alone or combined with synthetic fungicides. The timing of the fungicide applications was based on the risk forecasted by the decision support system RIMpro, and fertilisers were used at certain growth stages of the crop. The disease incidence on untreated fruits on cv. Auksis ranged from 38.3% to 59.6%, and on cv. Ligol from 99.3% to 99.5%. Strategies including synthetic fungicides were the most effective against scab on shoot leaves and fruits. The strategy using only inorganic fungicides was effective for low-inoculum orchards. Combining synthetic and inorganic fungicides provided the best apple scab control strategy on fruits, likely helping reduce the resistance selection pressure and residues of synthetic fungicides. Foliar fertilisers were insufficient to control apple scab; they would supplement existing scab fungicide programs.

The study is part of a larger project aimed at developing an integrated disease risk warning and early detection system for greenhouse-grown tomatoes, particularly for medium- and small-scale growers. Tomato plants (Solanum lycopersicum L. ‘Encore’ F1) were cultivated under controlled greenhouse conditions and artificially inoculated with Alternaria sp., the causal agent of early blight. Disease progression was monitored using a hyperspectral (HS) camera operating at 400–1000 nm, alongside visual disease assessments. Five experimental replicates were conducted, differing slightly in plant developmental stage at inoculation and post-inoculation temperature and relative humidity. A total of 1000 HS images were acquired, comprising 155 control and 845 inoculated leaves, although visible symptoms did not develop on all inoculated leaves. Mean visually assessed disease severity across replicates ranged from 0.1% to 17.7%. Principal component analysis (PCA) effectively discriminated between diseased and non-diseased tissues. The most informative spectral regions were associated with pigment absorption (beyond 550 nm), red-edge, and near-infrared domains, supporting their relevance for early detection of Alternaria-induced stress. As proof of concept, logistic regression-based leaf image segmentation achieved a mean cross-validation accuracy equal to 93.8%.