Spatial heterogeneity in crop physiological performance remains a major constraint to efficient management in tropical horticultural systems. This study evaluated the capability of UAV-based multispectral imagery to predict intrinsically derived physiological health states of tropical papaya by integrating canopy spectral predictors with multivariate plant functional measurements. Field observations were conducted on 103 papaya trees in a commercial plantation in Yogyakarta, Indonesia. Six physiological indicators, namely SPAD chlorophyll meter value, stomatal conductance (gsw), electron transport rate (etr), maximum fluorescence (Fm), steady-state fluorescence (Fs), and effective quantum yield of photosystem II (ΦPSII), were used to derive intrinsic health states through K-means clustering after z-score standardization. Candidate cluster numbers ( K = 2–5) were evaluated using inertia, silhouette coefficient, Calinski–Harabasz index, and Davies–Bouldin index. Although K = 2 yielded the most compact statistical partition, K = 3 was retained to preserve agronomically interpretable Healthy, Moderate, and Stressed physiological states. A total of 124 UAV-derived spectral predictors were constructed at the plant level and sequentially reduced to 21 predictors through correlation pruning and recursive feature elimination with cross-validation (RFECV). Random Forest classification using the selected predictors achieved a training accuracy of 0.958, testing accuracy of 0.903, and 5-fold cross-validation accuracy of 0.96 ± 0.02. Model interpretability identified GBNDVI_max, GBNDVI_mean, rho_green_min, MGRVI_std, RTVI_max, and MGRVI_min as key spectral features, while partial dependence analysis showed stronger threshold-like relationships with stomatal conductance than with ΦPSII. These findings indicate that UAV multispectral sensing can capture physiologically meaningful within-field heterogeneity in tropical papaya and support management-priority monitoring in precision agriculture.

Continuous cropping obstacles severely limit the sustainable development of global agriculture, creating an urgent need for broad-spectrum, efficient, and environmentally friendly biocontrol technologies. As the most extensively studied biocontrol fungi, Trichoderma exhibits notable advantages, including strong environmental adaptability and diverse biocontrol mechanisms. Unlike previous reviews that primarily focused on individual biocontrol mechanisms or plant disease suppression, this review integrates three major pathways through which Trichoderma alleviates continuous cropping obstacles: suppression of pathogenic fungi, modulation of the rhizosphere microbiome, and degradation of phenolic autotoxic substances. The biological characteristics and biocontrol mechanisms of Trichoderma are systematically summarized, including hyperparasitism, secretion of antimicrobial metabolites, competition for nutrients and space, and plant growth-promoting mechanisms via induction of systemic resistance through both ISR (induced systemic resistance) and SAR (systemic acquired resistance) pathways. More importantly, Trichoderma can secrete specific metabolites to reshape the rhizosphere microbiome, enrich beneficial microbial communities, and form synergistic disease suppression effects, with biocontrol efficiencies reaching 50%–80%. Certain strains also achieve degradation rates of phenolic autotoxic substances exceeding 80%. Based on these insights, future research should focus on the molecular mechanisms of Trichoderma biocontrol, multi-omics approaches, and the development of efficient composite microbial products, providing scientific support for sustainable agriculture and strategies to reduce chemical inputs while enhancing efficacy.

Understanding peanut maturity dynamics is critical for optimizing harvest timing, maximizing yield and kernel quality, and improving production sustainability. However, quantitative evaluations of maturity progression in Virginia (VA)-type cultivars remain limited in humid subtropical environments. This limitation is further complicated by the belowground and indeterminate nature of peanut pod development, which makes direct field assessment of maturity difficult and labor-intensive. This study presents a comprehensive multi-environment evaluation of peanut maturity dynamics of five VA-type cultivars (Bailey-II, Emery, Walton, NC-20, and Sullivan) using datasets from agronomic research trials, breeder seed trials, and commercial grower fields across VA and North Carolina in 2022–2024. 1162 field samples were collected through systematic pod sampling, and their maturity was determined using the pod blasting followed by mesocarp color classification (white to black). A Peanut Maturity Index ( PMI), calculated as the proportion of orange, brown, and black pods relative to total pods, was used to quantify maturity. Days after planting ( DAP) was the dominant driver of maturity development ( p < 0.001), with steady increases through 120 DAP and accelerated maturation between 140 and 160 DAP. Optimal maturity ( PMI = 0.65–0.75) occurred at 140–150 DAP for Bailey-II, Emery, Sullivan, and NC-20, whereas Walton matured latest (145–155 DAP). Significant genotype × environment interactions were detected, while the growth regulator effect was inconsistent. Comprehensive growing degree day ( GDD) computations provided more evidence with Bailey-II showing the strongest correlations ( r = 0.43–0.93), followed by NC-20, Emery, and Sullivan, while Walton exhibited the weakest and most variable relationships, reflecting its greater environmental sensitivity. These results provide one of the most extensive multiyear, multi-location quantifications of VA-type peanut maturity under subtropical production, offering data-driven insights to refine cultivar selection, improve harvest scheduling, and support sustainable peanut production.

Nepal's agriculture remains predominantly cereal-based, with rice occupying most of the cultivated area and forming the dietary staple. This dependence heightens vulnerability to climate shocks, market volatility, and nutritional imbalance. Despite long-standing policy commitments to crop diversification, evidence on progress and policy effectiveness remains limited and fragmented. This study systematically reviews agricultural policies to investigate the evolution of crop diversification in Nepal, evaluate the programs to promote crop diversification, and empirically assesses diversification trends from 1990 to 2024, highlighting key gaps and structural constraints. We used a mixed-methods approach. A systematic scoping review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework analyzed 15 periodic plans, 18 sectoral policies, and initially over 70 donor-funded projects. After screening, 23 programs were included for detailed analysis from 1956 to 2024. Complementarily, quantitative analysis using Simpson's Diversity Index (SDI) measured diversification trends over 32 years (1990–2022). SDI was also mapped district-wise for visual analysis of the changes in diversification over the past three decades. The systematic review recognizes that policies and programs have endorsed crop diversification as a strategy to enhance agricultural output, promote ecological balance and strengthen socio-economic resilience. Persistent challenges such as weak market linkages, poor storage and cold-chain infrastructure, quality assurance gaps, and fragmented value chains have constrained impact. Structural issues such as declining public investment, climate risks, and labor migration further impede progress. The quantitative analysis shows a modest increase in crop diversification over the past three decades, with a noticeable slowdown after 2015. This study provides the first longitudinal assessment linking policy evolution with diversification outcomes, emphasizing the need for integrated, value chain–oriented, and climate-resilient diversification strategies.

Microalgal biorefineries depend on hydrodynamic choices that govern light access, gas transfer, biomass recovery, and product release, yet rotational motion as an organizing design variable has not been systematically synthesized across the cultivation, harvesting, and downstream-processing stages. This review evaluates rotational motion as a bounded engineering concept, distinguishing mechanically imposed rotation, rotation-induced secondary flow, and rotation-enabled separation from broader mixing or circulation phenomena. A critical narrative synthesis is presented across mechanically agitated photobioreactors, rotation-dominated flow reactors, rotating algal biofilm systems, centrifugal and rotary harvesting devices, and rotationally intensified extraction units. Rather than ranking heterogeneous reports, the synthesis is built on transferable metrics - light–dark cycling, volumetric mass transfer coefficient ( k_La), mixing time, shear or energy dissipation, solids loading, recovery, and biomass quality. The analysis shows that rotation improves productivity and process integration when matched to strain physiology, product value, and scale, but can increase power demand, heating, fouling, and cell damage when defaulted. A five-step decision framework is therefore proposed to select rotational strategies by process objective, strain and product sensitivity, value and purity, scale and solids loading, and standardized reporting.

Durian husk is rich in lignocellulose and represents a promising target for recycling and value-added utilization. However, the complex structure of lignin and its protective association with carbohydrates pose significant challenges for efficient component separation. Conventional chemical methods are often operationally complex, energy-intensive, and environmentally unfriendly. In this study, a one-step green separation method was developed using a subcritical carbon dioxide (CO₂) environment in combination with an alcohol-based ternary deep eutectic solvent (DES) composed of choline chloride, ethylene glycol, and 4-chlorobenzenesulfonic acid. This method achieved a delignification rate of up to 91.5% and a cellulose retention rate of 84.7% under mild conditions ranging from 60 °C to 90 °C and varying CO₂ pressures. The separation parameters influenced the crystallinity index, hydrogen bonding interactions, and lignin unit distribution in the cellulose-enriched fraction. Moreover, the incorporation of subcritical CO₂ enhanced the acidity and proton donation capacity, thereby improving reaction efficiency. In summary, this study presents a mild, efficient, and environmentally friendly strategy for the selective separation of lignocellulose from natural biomass.

Root zone water redistribution and nitrate mobility are difficult to diagnose in semi-arid agricultural profiles because point observations are noisy and temporally uneven, while open-loop process models may drift after rainfall, irrigation and fertilizer events. This study presents a benchmark-aware, observation-role-audited Ensemble Kalman Filter (EnKF) workflow for plot-depth soil moisture and nitrate state estimation in a maize field at Lawra, Upper West Region, Ghana, from 1 June to 13 September 2025. Calibrated observations at 10, 30 and 60 cm were integrated with weather forcing, irrigation and fertilizer event logs, management records and laboratory nitrate support within a layered soil-water-nutrient model. The contribution is not a new Kalman-filter algorithm, but a reproducible field-scale protocol combining asynchronous moisture-nitrate updating, calibration-update-validation role separation, benchmark-aware interpretation and water-conditioned nitrate-risk diagnosis. Relative to the open-loop model, EnKF assimilation reduced full-period soil moisture RMSE from 0.0902 to 0.0336 m³ m⁻³ and MAE from 0.0801 to 0.0297 m³ m⁻³; validation-period RMSE declined from 0.1038 to 0.0386 m³ m⁻³. Sensitivity testing showed that soil moisture RMSE remained below the open-loop value across tested ensemble size, covariance, inflation and update-frequency settings, with greatest deterioration under daily rather than hourly updating. Forecast error increased with lead time: moisture RMSE rose from 0.0176 m³ m⁻³ at 1 day to 0.0687 m³ m⁻³ at 7 days, while nitrate RMSE rose from 2.27 to 4.37 mg N kg⁻¹. Because aggregate NSE values remained negative, the workflow is presented as drift correction and short-horizon diagnostic state estimation, not proof of reduced nitrate loss or agronomic improvement.

The remediation of paddy fields contaminated by cadmium (Cd) and arsenic (As) is a global concern. In the present study, the field experiments were conducted to evaluate the effectiveness of foliar-applied [Glu][H₂PO₄] (Glu = glutamate) in reducing Cd and As accumulation in rice grains, while a hydroponic experiment under controlled Cd/As stress was used to examine whether this response was associated with changes in subcellular distribution and selected physiological indicators. Field experiments showed that foliar spraying with 0.2–1.5 mmol/L [Glu][H₂PO₄] reduced Cd accumulation by 6%–27% and 6%–26%, and As accumulation by 8%–24% and 8%–30% in early and late rice, respectively. In the hydroponic experiments, 1.5 mmol/L [Glu][H₂PO₄] was associated with markedly lower Cd and As contents in both roots and shoots. This treatment also coincided with a greater proportion of Cd in the cell-wall fraction, a greater proportion of As in the soluble fraction, and changes in selected chelation-related, antioxidant-related, and transporter-related indicators. Together, these results support the potential of foliar-applied [Glu][H₂PO₄] as a mitigation strategy for Cd/As-contaminated rice production and suggest that altered intracellular partitioning and detoxification-related responses may contribute to the reduced accumulation observed. However, the route-specific mechanism linking foliar application in the field to the responses observed in the seedling assay remains to be further clarified.