Agrifood Research Finland

Introduction The multiple nutritional disorders producing the early decline of citrus productivity are commonly observed across the citrus belts of northeast India. This situation is further compounded by a mismatch between annual addition and consumption of fertilizers, in the backdrop of an erroneous diagnosis of nutrient imbalance. In this background, we attempted to diagnose nutrient balance in Khasi mandarin ( Citrus reticulata Blanco) orchards using several diagnostic tools comprising machine learning (ML) tools. Methods A database of soil available nutrients (KMn0 4 -N, Brays-P, NH 4 0Ac-K, DTPA-Fe, DTPA-Mn, DTPA-Cu, DTPA-Zn) and fruit yield documenting 180 Khasi mandarin orchards of seedling origin (10–30 years old with row-to-row distance of 4 m and trees 6 m apart) raised under rainfed conditions in the Meghalaya state of northeast India. Diagnosis methods were compared: the sufficiency level of available nutrients (SLAN), the basic cation saturation ratio (BCSR), the compositional nutrient diagnosis (CND), the diagnosis and recommendation integrated system (DRIS) and ML tools like random forest and xgboost. Results Soil test interpretation of a low-yielding and nutritionally imbalanced orchard differed among diagnostic methods. DRIS predicted deficient-to-low concentrations of Zn, Ca, P, N, and K; other nutrients like Fe, Cu, Mn, and Mg were at optimum-to-high concentrations. CND standards diagnosed Zn deficiency and Cu excess with potential agronomic manifestations for early decline in productivity. SLAN interpretation was highly skewed for Ca; moderately skewed for Mg, Cu and Zn, and unskewed for N, P, K, Fe and Mn. The accuracy of ML regression models relating nutrient expressions to fruit yield was invariably high, followed by SLAN. The ML xgboost regression model exhibited the highest accuracy in predicting fruit yield from soil test. Conversely, the BCSR, which considers only three cationic dual ratios, was inaccurate. There were distortions when relating concentration values to DRIS indices to determine ‘optimum’ concentration ranges. The ML classification models showed that concentration values were also less accurate than clr to classify data as true negative (TN) or true positive (TP). The xgboost classification model showed optimum ranges for N, P, Ca, Mg, Mn, and Cu; whereas K, Fe, and Zn fell below the lower limits. Conclusion ML, hence, as a classification approach, aided in discarding cases of poor yields and high yields showing luxury nutrient consumption or suboptimal nutrient levels. The soil test standards and site-by-site comparisons can further support site-specific nutrient management and precision fertilization. Considering Zn as the most deficient nutrient, Zn biofortification interventions are recommended to increase fruit yield and quality in Khasi mandarin.

Diabetes mellitus remains a major global health burden, and many patients do not achieve durable glycemic control despite modern pharmacotherapy. This narrative review synthesizes evidence on plant-derived strategies that may complement standard care, focusing on two clinically aligned domains: glucose-lowering medicinal plants and plant-based sugar substitutes that reduce dietary glycemic load. We summarize key mechanistic pathways, including inhibition of α-amylase/α-glucosidase, reduced intestinal glucose entry and absorption kinetics, glucose-dependent insulinotropic effects, improved insulin signaling, suppression of hepatic gluconeogenesis, and microbiota-linked effects. We critically appraise human evidence for selected botanicals (cinnamon, fenugreek, mulberry, gymnema, gynura, rosehip, and Jerusalem artichoke) and plant sweeteners (stevia and monk fruit). Overall, clinical effects are modest and heterogeneous; the most reproducible signals are observed for mulberry leaf in blunting postprandial glucose excursions, and for cinnamon, fenugreek, and gymnema, where meta-analyses suggest modest improvements in glycemic markers. Stevia and monk fruit are best supported as glycemically neutral sucrose substitutes, while inulin-type fructans show small-to-moderate benefits with sustained intake, limited by gastrointestinal tolerability at higher doses. Key gaps include a shortage of long-term randomized trials using standardized preparations and durable endpoints such as glycated hemoglobin. Plant-derived interventions are therefore best positioned as adjuncts within individualized, evidence-based glycemic management.

Societal Impact Statement Plants provide oxygen, food, shelter, medicines and environmental services, without which human society could not exist. Tackling pressing and global challenges requires well‐trained plant scientists and plant‐aware individuals. This manifesto provides a practical evidence‐based vision to strengthen plant science education, focused on five strategic priorities. It is relevant to all stakeholders within plant science and beyond: from frontline educators to institutional leaders; from commercial or charitable professionals to entrepreneurs and donors; from individual community members to their legislative representatives. Strengthening plant science education demands concrete actions from all stakeholders, ultimately to the benefit of us all. Summary Plant science education needs urgent attention. Skilled plant scientists are needed to address major environmental and societal challenges, and global communities require plant‐aware professionals to drive impactful policy, research and environmental stewardship. This manifesto was collaboratively generated by a community of educators who gathered to reflect on the state of plant science education. The forward‐facing document provides a clear strategy for plant science education, complementing existing research strategies. Five themes were identified as essential for meeting the evolving needs of plant science, educators and learners: (i) plants must be at the centre of an education that addresses global challenges and societal values; (ii) plant science education must prepare students for their futures using bold and effective pedagogies; (iii) equity, diversity and inclusion must be robustly embedded in educational practices; (iv) local and strategic partnerships (with industry and beyond) are required to strengthen academic education; and (v) plant science educators need resources and opportunities to develop and connect. The manifesto is intended as a framework for change. Educators, funders, publishers, industry representatives, policymakers and all other members of our communities must commit to sustained investment in plant science education. By proactively and collectively embracing the recommendations provided, the sector has an opportunity to cultivate a new generation equipped with the knowledge, skills and passion to unlock the full potential of photosynthetic organisms.

Abstract The South Russian wolf spider ( Lycosa singoriensis ), a large thermophilous species native to Eurasian steppe regions, has shown marked distribution shifts at the northwestern edge of its range in Central Europe, particularly in Czechia. This study synthesizes historical and recent occurrence records of L. singoriensis in Czechia from 1924 to 2023 (n = 89), integrating species data with long-term weather observations from southeastern Czechia. The results reveal two major temporal clusters of records: an initial expansion from the 1920s to the 1950s, and a resurgence beginning in the early 2000s. Both periods coincide with climatologically anomalous summers characterized by above-average temperatures and below-average precipitation. Conversely, the mid-20th century, especially the 1950s–1990s, lacked such anomalies and coincided with the species’ apparent local extinction. Habitat analysis confirmed strong associations with thermophilic open environments, including anthropogenic landscapes, such as gravel-sand pits and military training areas. The study highlights climate change as the primary driver of the species’ regional dynamics, with land-use change playing a secondary role. The conservation implications of these findings lie in recognizing military training areas and industrial wastelands as critical habitats for L. singoriensis and other steppe-adapted species. This study also highlights the importance of combining biodiversity records with long-term weather records to guide monitoring and conservation planning under climate change.

The growing complexity of global food security, exacerbated by climate change and socio-economic disparities, calls for a multi-hazard approach to risk evaluation and management. Recognizing the lack of a universally accepted measure for food insecurity covering all dimensions, we first review target variables and input features in existing ML modeling efforts, providing an assessment of current data availability, accessibility, and fragmentation, and improving the understanding of possibilities and limitations of ML for the food security community.  We further consolidate a comprehensive dataset, with an operational design for continuous enrichment, that includes various indicators and precursors, updated monthly on a subnational level across over one hundred countries. We apply innovative explainable artificial intelligence (XAI) methods to unravel the intricate relationships between food insecurity, drought, and conflict-related fatalities. Our models forecast food crises with different lead times, revealing the nuanced patterns recognized by machine learning algorithms over various time frames. Our analysis also shows that the relative importance of drivers can shift depending on the food security metric used, indicating that distinct processes are at play in its many dimensions. This study not only exposes the complex drivers of food security but also provides policymakers with an operational multi-risk forecasting tool, improving the ability to foresee and strategically manage food crises.   

Glyphosate [N-(phosphonomethyl) glycine] (GLY) adsorbs strongly in Finnish soils. A new method for GLY and its main degradation product, aminomethylphosphonic acid (AMPA) residues in clay soils (Protovertic Luvisol) was developed and validated. A new method was necessary because the previous one required laborious cleaning pre-treatments, and its recovery was quite poor (<40%–70%). In the new method, the earlier method's extraction solvent, 0.1 M potassium hydroxide (KOH), was replaced by more effective 0.6 M KOH. The old post-column high-performance liquid chromatography and fluorescence (HPLC-FLD) method was replaced by the ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) method. Compounds were identified as their fluorenyl methyl chloroformate (FMOC) derivatives by a multiple reaction monitoring (MRM) technique and quantified by an internal standard method utilising multipoint matrix-matched calibration. Glufosinate-ammonium (GLUF) was used to monitor the effectiveness of extraction with good recovery (80–119%). All calibration curves were found to be linear (R2 ≥ 0.98) in the studied calibration range (0.01–3.31 mg kg−1 in fresh soil). The repeatability and reproducibility were 25% and 28% for GLY, and 20% and 24% for AMPA in real research soil samples. The method was effective throughout the calibration range in all the studied Finnish agricultural soils.•An improved method was created to analyse glyphosate (GLY) and AMPA in Finnish clay soil.•The challenge caused by strong GLY adsorption on soil was solved by using multipoint matrix-matched calibration curve samples which were prepared identically with the research samples.•The method performed well in all tested clay, loam and sandy loam soils.