Abstract: Drought stress is a major factor limiting seed germination and seedling establishment in crops. Here, two proso millet (Panicum miliaceum L.) varieties were used to evaluate the effects of 2,4-epibrassinolide (EBR) seed priming on germination under polyethylene glycol (PEG)-simulated drought stress. Seeds primed with different EBR concentrations were assessed for germination traits, seedling growth, water absorption, and α-amylase activity. Drought stress markedly reduced germination potential, germination rate, germination index, and vigor index, while prolonging mean germination time. EBR priming alleviated these adverse effects, with 0.01 µmol·L⁻¹ showing the most consistent and effective promotion of germination and early seedling vigor under drought conditions. The two varieties exhibited different response patterns to EBR, indicating genotype-dependent sensiortivity. Moreover, EBR priming enhanced seed water uptake and maintained higher α-amylase activity, suggesting improved reserve mobilization and energy supply during germination. In conclusion, optimal EBR priming effectively mitigates drought-induced inhibition of proso millet germination and provides a useful strategy for improving seedling establishment under water-limited conditions.

Abstract: Most studies in the field of application technology have focused on the interaction between adjuvants and agrochemicals, highlighting the need for further research to evaluate the behavior of adjuvants in association with other classes of crop protection products. In this context, the objective of this study was to evaluate the influence of adjuvants and air velocity on spray drift deposition in simulated applications conducted in a wind tunnel using a bioinsecticide based on Bacillus thuringiensis. The experiment was carried out in an open-circuit, blower-type wind tunnel installed at the Agricultural Machinery Laboratory of the State University of Goiás – Central Campus. The study was conducted in a completely randomized design arranged in a 5 × 4 × 4 factorial scheme, with three replications. Treatments consisted of five horizontal distances from the spraying point (0.45, 0.75, 1.05, 1.35, and 1.65 m), four wind speeds inside the tunnel (1, 2, 3, and 4 m s⁻¹), and four spray solution formulations (water + dye; bioinsecticide + dye; bioinsecticide + vegetable oil + dye; and bioinsecticide + surfactant + dye). For deposition sampling, artificial targets were positioned transversely to the airflow and, immediately after spraying, were divided into lower, middle, and upper thirds relative to the height of the test section. Data were obtained by spectrophotometry and, after verifying the ANOVA assumptions, subjected to ANOVA (P < 0.05). When significant, mean comparisons and regression analyses were performed. Statistical analyses were conducted using the R and Sisvar software packages. Mean deposition values were converted into drift percentage as a function of the total sprayed volume. The experimental data were also subjected to geostatistical analysis using GS+ software (Version 7®). After confirming spatial dependence, contour maps were generated using kriging. Higher wind speeds led to higher drift percentages. The use of the adjuvant Break Thru® contributed to reducing drift in the upper and middle thirds. In the lower third, at a wind speed of 1 m s⁻¹, the addition of Break Thru® reduced drift; however, at 4 m s⁻¹, adjuvants did not reduce deposition compared to the spray solution prepared with the biological insecticide. The analyzed variable showed a strong spatial dependence across all treatments.

Abstract: The use of microbial inoculants is a promising and sustainable alternative to agrochemicals. However, their field efficacy is inconsistent. This review critically evaluates the scientific basis for using microbial inoculants in modern agriculture, analyzing their complex interactions within agroecosystems. We demonstrate that the effectiveness of inoculants is governed by predictable ecological principles, rather than random processes. The formation of plant microbiomes follows distinct, deterministic patterns, with specific colonization patterns for each compartment and a strong influence from soil type and climate. Furthermore, this review demonstrates that, for plant-beneficial microorganisms used as bioinoculants, their antimicrobial metabolites function not merely as weapons, but as sophisticated ecosystem engineers that selectively reshape microbial communities. Compounds of plant growth-promoting microorganisms like cyclic lipopeptides, macrolactins, 2,4-DAPG, and gliotoxin demonstrate dose-dependent regulatory effects, enhancing specific soil functions while maintaining community stability. The transition from microbial monocultures to synergistic consortia proves essential, though success depends on matching inoculant composition to plant chemical signaling profiles. Practical recommendations include prioritizing native stress-tolerant strains, implementing soil-specific formulations, and developing metabolite-based preparations that function as ecological modulators rather than broad-spectrum suppressants. This ecological framework provides the scientific foundation for the next generation of predictable and effective microbial inoculants.

Abstract: Background: Climate projections for western Canada predict reduced water availability and more frequent heatwaves, underscoring the need to improve water-use efficiency and heat tolerance to sustain crop productivity and grain quality. Materials and Methods: A total of 198 historical and modern Canadian spring wheat cultivars were evaluated under water-deficient and high-temperature conditions. Measurements included whole-plant and leaf-level WUE, carbon isotope discrimination (δ¹³C) in flag leaves, and physiological traits such as leaf water potential, photosynthetically active radiation, and chlorophyll fluorescence parameters (F₀, FV/FM, FM, FV, φDo, and ETR) across six growth stages. Results: WUEWP showed a weak relationship with δ¹³C, indicating strong environmental and genetic in-fluences and limiting its reliability as a proxy across conditions. Spring wheat cultivars exhibited low genetic diversity for WUEWP and heat tolerance, suggesting limited adaptive capacity to increasing stress. Multivariate analyses (PCA and clustering) effectively captured trait variation and differentiated cultivars. Chlorophyll fluorescence traits sensitively reflected reductions in photosynthetic efficiency under drought and heat stress. Conclusion: Overall, the results indicate meaningful genotypic variation but limited genetic diversity and weak relationships among WUE, δ¹³C, and related traits, highlighting the need for new germplasm and integrated phenotyping to enhance selection efficiency and develop more climate-resilient spring wheat.

Abstract: The demand for sustainable weed management and the limited discovery of new herbicide molecules have led to high interest in plant-derived bioherbicides, such as the water residues (WRs) from the hydrodistillation of aromatic plants, which contain biologically active secondary metabolites. Here, the phytotoxic potential of WRs of four aromatic plant species was investigated. Chemical composition of WRs was determined by SPME–GC–MS, and their effect was assessed on seed germination and seedling growth characteristics of Avena sterilis, Echinochloa crus-galli, and Zea mays. Five concentrations, i.e., 0, 10, 20, 50, and 100, with 100 representing pure WR were tested. Phenolic monoterpenes dominate WRs in oregano and thyme, and oxygenated monoterpenes in laurel and lavender. Germination and growth responses were dose-dependent and species-specific. Oregano and lavender WRs exhibited the strongest phytotoxicity, reducing weed germination by 82% and 79%, respectively. In contrast, laurel extracts showed weaker germination inhibition. Across all tested species, germination delays were observed, making WRs a promising candidate for weed control. The results also showed that WR affected root growth by up to 95% shoot by 70–80%. Maize exhibited greater tolerance than the weed species maintaining higher germination. Overall, WRs represent a promising tool for integrated weed management.

Abstract: This study evaluated the effect of byproduct from mining process of granite rock doses in cultures of high demand in soil fertility with annual and perennial cycles, such as soybean and Tamani perennial grass, for two years in the municipality of Paragominas-PA. Experimental design was in randomized blocks comprising five treatments and five replications. Treatments comprised doses of byproduct from granite rock of 1000, 2000, 4000 and 6000 kg ha-1 and a control treatment (without application) applied in soybean and Tamani perennial grass. Soil parameters and crops productivity were evaluated for two years. The higher doses showed positive effects on soil fertility parameters, including potassium increases. Crops productivity had low responses to application or residual effects of the byproduct from granite rock mining process from Tracuateua-PA. The byproduct from mining process of granite rock has low influence in soil fertility and yield of soybean and Tamani perennial grass.

Abstract: Microbial-based products are essential for sustainable agriculture, yet inconsistent performance and limited mechanistic understanding constrain their adoption. While terminology varies globally—from "bioinputs" to "microbial products"—this linguistic diversity reflects a deeper conceptual gap. Historically, the sector has relied on a successful but empirical Bioinputs 1.0 paradigm, based on phenotypic screening and a "black box" approach to efficacy. We propose Bioinputs 2.0 as an evolutionary framework grounded in genomics, functional biology, and advanced formulation. This paradigm integrates microbial ecology, metabolite-driven bioactivity, and systems-level interactions, positioning formulation as an integral design component rather than a secondary step. Transitioning from empirical discovery to knowledge-driven design is necessary to ensure reliable, scalable applications. While particularly evident in biocontrol, this shift provides a stronger basis for interpreting field responses in plant growth-promoting microorganisms. Overall, Bioinputs 2.0 emphasizes integrated, context-dependent biological systems to bridge the gap between laboratory insights and consistent field performance.

Abstract: This working paper proposes an integrated framework that combines digital twin technology, artificial intelligence, immune-inspired regulation, and adaptive memory to support viability-oriented decision-making in agricultural supply chains under disruption. The study is motivated by the growing interest in digital twins in both supply chain and agricultural research, alongside the limited development of frameworks capable of moving beyond monitoring and prediction toward dynamic regulation and learning. In response to this gap, the proposed architecture is structured around four tightly coupled components: a digital twin as the cyber-physical representation layer, an AI-driven state estimation and predictive modeling module, the Supply Chain Immune System (SCIS) as the regulatory layer, and Immune-Structural Adaptive Response (RAIE) as the adaptive memory layer. These components are formalized through a dynamic system in which performance evolves according to disruption effects, corrective actions, and accumulated experience. To illustrate the behavior of the framework, simulation experiments were conducted under baseline, single-disruption, and repeated-disruption scenarios. The results show that predictive capabilities improve anticipatory response, but their effect remains limited when not supported by adaptive regulation. In contrast, the integration of SCIS and RAIE leads to faster recovery, lower performance degradation, and more stable behavior under recurrent disturbances. The findings suggest that viability in agricultural supply chains depends not only on visibility and prediction, but also on the coordinated interaction of representation, control, and learning. The study contributes a conceptual and computational foundation for advancing digital twins in agriculture toward adaptive, disruption-aware, and viability-oriented systems.

Abstract: In plant factories with artificial lighting (PFALs), spectral regulation serves as the predominant factor governing plant growth and development. The implementation of red-enriched spectral regimens during cultivation promotes biomass accumulation, whereas blue-dominant spectra enhance the biosynthesis of phytochemicals and nutritional compounds in plants. Nevertheless, systematic investigations of staged spectral regimens on both plant development and secondary metabolite biosynthesis remain limited. This study implemented a staged lighting regimen utilizing three distinct red-to-white photon flux ratios (R: W=3:1, 1:1, and 1:3) administered sequentially during critical developmental phases: seedling stage, the early growth stage (15 d after transplanting, DPA), and the late growth stage (16-30 d, DPA) in Pak choi. This study implemented four distinct staged spectral regimens to evaluate photonic treatment effects through multivariate analysis of biomass production, morphological development, photosynthetic pigments, nutritional metabolites, along with antioxidants and radical quenching capacity. The results demonstrated that the T4 treatment significantly enhanced biomass production across all developmental stages. While, the T3 treatment exhibited optimal efficacy in improving nutritional quality (particularly content of soluble proteins and Vitamin C) along with superior antioxidant capacity. The higher red-light significantly enhanced leaf expansion and carotenoid biosynthesis at the seedling stage. While higher blue-light in subsequent growth stages effectively stimulated biosynthesis of chlorophyll and antioxidants. This study established that temporal modulation of red-to-white spectral ratios during vegetative development enabled synergistic optimization of yield and quality attributes in Pak choi.

Abstract: Potassium (K+) is an essential mineral element for plant growth and development. Members of the HAK/KUP/KT (HAK) gene family play key roles in K+ uptake and ho-meostasis. Although many of them have been extensively identified in a variety of plant species, sweet potato has not yet undergone systemic characterization. In this work, 22 potential IbHAK genes are identified based on phylogenetic analysis, and categorized into four groups (I-IV). We performed comprehensive analysis of IbHAK genes, including protein property, chromosome localization, gene structure, collinear-ity and promoter cis-element investigations for each one. Five IbHAK5 proteins (IbHAK5A-IbHAK5E) were found on the same branch as AtHAK5, OsHAK5, and ZmHAK5, suggesting that small-scale duplication events contributed to the expansion of IbHAK5s in sweet potato. IbHAK5A, a gene highly expressed in various tissues and significantly induced under low-K+ (LK) stress, was cloned and functionally charac-terized in potassium transporter deficient yeast and transgenic Arabidopsis. An AP2/EREBP family transcription factor, IbPTL1, was subsequent identified as having the ability to bind the IbHAK5A promoter and playing a role in regulating the K+ sig-naling pathway. This study provides a foundation for further functional characteriza-tion of HAK/KUP /KT transporters in sweet potato and key candidate genes for further functional analysis, which may be useful for breeding sweet potato that utilizes potas-sium more efficiently in the future.

Abstract: This study addresses the challenge of detecting white grape clusters (Vitis vinifera L) in high-density vineyard canopies, a critical task for precision viticulture and yield estimation. Traditional statistical and image-processing methods have struggled with occlusion issues. In this work, over 100 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation, with various preprocessing strategies tested to enhance cluster visibility. Convolutional Neural Network (CNN) architectures were compared, highlighting YOLOv8 as superior to Mask R-CNN in both accuracy and efficiency. YOLOv8, trained for up to 100 epochs on equalized and augmented datasets, achieved outstanding performance: 84.9% precision, 72.6% recall, and mAP@0.5 of 83%, far surpassing Mask R-CNN (17% precision, 26% recall). The model successfully detected partially hidden clusters, including those invisible to human experts, better than previous studies that required controlled backgrounds or artificial lighting. Results confirm that combining RGB equalization with data augmentation optimizes detection. These findings underscore the potential of deep learning and low-cost RGB imaging systems to enable automated, scalable solutions for yield estimation and canopy analysis. In conclusion, YOLOv8 emerges as a promising tool for accurate grape bunch detection under field conditions, overcoming previous limitations.

Abstract: The nutritional value of fruit is essential for human health; however, these attributes may be lost or degraded during storage. Fruit behavior during the storage period is strongly influenced by pre-harvest production factors. In this study, biochemical quality parameters were analyzed post-harvest, as well as during and after storage in ultra-low oxygen (ULO) conditions. Three pear varieties ('Williams', 'Abate Fetel', and 'Conference') were evaluated on two rootstocks (quince and wild pear seedling) across three plot positions (top, middle, and base) over two consecutive years. The analyses included total phenolic content (TPC), antioxidant capacity (AOC), soluble solids content (SSC), and individual sugar profiles (glucose, fructose, sucrose, and sorbitol). The results indicate that the root-stock significantly influenced the analyzed fruit characteristics in cultivars investigated. Rootstock had a highly statistically significant impact on SSC of 'Abate Fetel' and 'Conference', whereas the difference was not significant for 'Williams'. All cultivars showed lower TPC and AOC on seedling rootstock. Conversely, the effect of tree position on the studied parameters and the stability of nutritional traits during storage was not statistically significant. It was concluded that the cultivars exhibited distinct behaviors under the storage regime depending on the factors observed. During storage, 'Williams' maintained a high nutritional value, particularly regarding phenolic and sugar content and strong antioxidant capacity. In contrast, 'Abate Fetel' and 'Conference' showed a decline in nutritional properties, which adversely affected overall fruit quality.

Abstract: Monitoring genetic gain is critical for evaluating breeding program performance. This study assessed genetic trends in the Zambia national maize breeding program using his-torical data (2001–2017) from 2,225 hybrids tested across years and locations. Best linear unbiased estimates (BLUEs) were calculated, and genetic trends were determined by re-gressing entry means on first-year testing data. Mean heritability was moderate for grain yield, plant height, and ear height, and high for anthesis and silking dates, indicating strong reliability for flowering traits. Significant positive genetic gains were observed for most traits except days to silking. Grain yield increased at 0.021 t ha⁻¹ per year (0.85% annually), reflecting progress but remaining below levels required to meet future production demands. Plant and ear height increased by more than 1.3 cm annually, suggesting directional selection for taller plant architecture. Grain texture declined by 1.28% per year, indicating a shift toward flint-type kernels. Anthesis date and ears per plant showed minimal genetic variation. Regression models explained over 15% of the total variation for plant height, ear height, ear number, and grain texture, confirming consistent genetic progress. Although measurable gains were achieved, accelerating yield improvement will require rapid-cycle breeding, enhanced trait heritability, modern breeding tools, and strategic reallocation of resources to sustain long-term impact.

Abstract: Accurate crop yield prediction is essential for improving agricultural productivity, resource management, and food security, particularly in heterogeneous environments such as Colombia. Recent advances in machine learning have enhanced predictive capabilities; however, most existing approaches rely predominantly on climatic or image-based data, limiting their direct applicability to agronomic decision-making. This study proposes a machine learning framework for crop yield classification based on foliar nutrient analysis, fertilization practices, and geographic variables using open-access agricultural data. The approach formulates yield prediction as a multi-class classification problem, enabling the identification of performance levels that are more interpretable and actionable in practical contexts. Four machine learning models—Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting—were evaluated. The results show that ensemble-based methods outperform alternative approaches, with Random Forest achieving the highest accuracy (96.27%) and macro F1-score (0.9261), followed by Gradient Boosting (95.34%). Feature importance analysis reveals that geographic location, crop type, and foliar nutrients such as sulphur, nitrogen, magnesium, calcium, potassium, and zinc are the most influential predictors. These findings demonstrate that nutrient-based variables provide a direct and meaningful representation of crop performance, offering advantages over models based solely on environmental proxies. By integrating foliar analysis with management practices, the proposed framework enhances interpretability and supports agronomic decision-making, contributing to the advancement of precision agriculture in data-scarce and heterogeneous contexts.

Abstract: This study addresses the problem of designing viable first-mile agricultural supply chains under uncertainty, with a focus on small-scale production systems characterized by territorial heterogeneity and logistical constraints. A unified modeling framework is proposed, integrating a data-driven Rural Viability Index (RVI), a Unified Robust Stochastic Programming (URSP) formulation, and adaptive mechanisms inspired by the Human Immune System (HIS). The approach combines territorial assessment, uncertainty management, and endogenous adaptation within a single decision-making structure. The framework is applied to a fresh pea supply chain in Cundinamarca, Colombia, considering multiple uncertainty regimes and adaptive configurations. The results show that system performance is strongly influenced by the interaction between URSP weighting and the activation of adaptive mechanisms. Configurations with HIS activation consistently achieve higher net utility, improved viability indicators, and greater supply capacity, especially under scenarios with a stronger emphasis on disruptive conditions. At the same time, the analysis reveals trade-offs in environmental impact and labor demand, highlighting the multi-dimensional nature of supply chain viability. The findings demonstrate that viability is not achieved through isolated optimization components, but through the coordinated integration of territorial intelligence, uncertainty modeling, and adaptive response capabilities. From a methodological perspective, the study contributes by operationalizing viability within a unified optimization framework, bridging the gap between predictive analytics and prescriptive modeling. From a practical standpoint, the results suggest that first-mile supply chain design should incorporate adaptive mechanisms and spatial information as core elements to ensure sustained performance under uncertainty.

Abstract: Crop yield prediction is essential for enhancing agricultural productivity, managing risks, ensuring food security, and improving the sustainability of farming systems. This study aimed to evaluate the effectiveness of Random Forest (RF) regression for predicting wheat yield using a time-series dataset comprising climate- and soil-related variables, and to identify the key factors influencing wheat yield. The performance of RF was compared with Multiple Linear Regression (MLR) as a benchmark model. The results showed that RF outperformed MLR in predicting wheat yield. Model performance was evaluated using mean absolute error (MAE) and root mean squared error (RMSE). The RF model achieved an MAE of 135.88 and an RMSE of 163.90, whereas the MLR model produced substantially higher errors, with an MAE of 435.74 and an RMSE of 653.39. Variable importance analysis from the RF model indicated that year and CO₂ emission were the most influential predictors. Nitrogen fertilizer use, wheat cultivated area, and phosphate fertilizer application were also associated with improved wheat yield. The partial dependence plot for year revealed an increasing trend in wheat yield from 2000 to 2010, followed by a yield plateau after 2010. Overall, these findings demonstrate that RF provides superior predictive performance compared with MLR and represents a robust tool for wheat yield forecasting and identifying key drivers of yield variation.

Abstract: Against the backdrop of global warming and expanding saline-alkali land, cultivating salt-tolerant rice is critical for safeguarding China’s food security. As a major coastal agricultural region, Zhanjiang in Guangdong Province provides an important study site to explore insect diversity in salt-tolerant rice ecosystems, which is key to understanding local species diversity. This study aims to conduct research on the insect diversity of the salt-tolerant rice meadow in Zhanjiang, to understand the species composition and transmission of insects in this ecosystem. This study uses hemp nets to simultaneously collect insects from the Haihong Aromatic Rice, wild sea rice, and the growth areas of conventional rice in the Zhanjiang area. After collecting the insects and conducting classification and discovery, The social structures of these insects and the diversity existing among them were studied. Based on classification results, a total of 270 insect species were identified across nine orders (Coleoptera, Diptera, Hymenoptera, Lepidoptera, Hemiptera, Orthoptera, Odonata, and Alata). Diversity analysis showed rich insect diversity in salt-tolerant rice fields, where Diptera and Hemiptera dominated the overall population.

Abstract: In Italy, the apple cider market is experiencing significant growth, driven by numerous small-scale artisanal producers who combine local apple varieties with traditional processes to offer complex, and diverse products. However, artisanal production based on spontaneous fermentations often encounters challenges in qualitative reproducibility, particularly related to sensory issues (stability across different vintages and high turbidity of the product). In this context, a methodology has been developed to optimize the technological process of cider production at Contrada Contro in the Monti Sibillini (MC), in Marche region, Italy. The research focused on the isolation and selec-tion of indigenous yeasts from frozen must prepared in the 2023 vintage. Following isolation and preliminary characterization, the indigenous yeasts were used to referment the still cider, followed by 7 months of bottle aging, and a second sampling point was conducted after 14 months of aging on lees. Destructive analyses using HPLC-DAD and GC-MS were conducted to evaluate poly-phenols and volatile compounds, while non-destructive analyses with a 12-quartz microbalance electronic nose and NIR spectroscopy allowed for a quicker assessment of production techniques. Chromatographic analysis results showed that the sensory quality of refermented products was strongly influenced by the composition of the yeast strains used. All fermentations inoculated with selected yeasts exhibited lower turbidity compared to spontaneous fermentation. These findings indicate that the selection of indigenous yeasts for cider refermentation enables the production of a high-quality product, enriched with beneficial compounds and characterized by a strong terroir identity, underscoring the importance of microbiological terroir.

Abstract:

Protein hydrolysate-based biostimulants have been widely investigated for their potential to enhance seedling growth; however, integrated interpretations of dose-dependent morphophysiological and photochemical responses remain limited for Theobroma cacao L., particularly during the nursery phase. This study aimed to evaluate dose-dependent physiological modulation in cacao seedlings of the Catongo and TSH 1188 genotypes under increasing concentrations of a protein hydrolysate-based biostimulant. The experiment was conducted in a randomized block design arranged in a 2 × 6 factorial scheme, corresponding to two genotypes and six biostimulant concentrations. Morphological traits, biomass accumulation, morphophysiological indices, chlorophyll indices, and chlorophyll a fluorescence parameters were assessed. The results revealed clear dose- and genotype-dependent responses, with predominantly quadratic patterns across most variables. Intermediate concentrations were associated with coordinated improvements in vegetative growth, root development, and photosystem II (PSII) functional performance, whereas higher concentrations were linked to reduced physiological balance. Overall, the findings indicate that protein hydrolysate-based formulations modulate cacao seedling performance through dose-dependent physiological adjustment, contributing to a more integrated understanding of biostimulant action during the nursery phase.

Abstract: Common bunt is a serious disease of wheat that may lead to yield losses in many regions of the world and can reduce yield and flour quality. Fifty wheat heads were randomly sampled from each of the six fields during a survey for disease diagnosis during the 2020 cropping season from the Seenlast area of district Chitral located in the Hindu Kush region of Pakistan. Field observations were recorded and diseased heads, bunted balls, and spores derived from bunted balls were morphologically examined using stereo and compound microscopy. Based on the disease symptoms and teliospore morphology of the studied 300 samples, the fungus was identified as Tilletia laevis, the cause of the common bunt of wheat. Disease prevalence was detected in 10-36% of the studied samples. It is the first report of common bunt occurrence from Chitral, the northernmost district of Pakistan, which confirmed the re-emergence of the disease after three decades in the country.