Bioinoculant production requires multidisciplinary expertise to ensure reliable field performance. This study evaluated four native rhizobacterial isolates from the cocoa rhizosphere for inclusion in biofertilizing microbial consortia, assessing effects on soil enzymatic activity, physicochemical properties, and dasometric variables under nursery conditions in Pauna, Boyacá - Colombia. Seven treatments were compared: two native consortia (single-dose and serial application), a commercial inoculant, a chemical fertilizer, and an uninoculated control, using a completely randomized design with five replicate blocks of 20 plants per treatment. Three composite soil samples per treatment were analyzed at 0 and 6 months. Native consortia comprised phosphorus-solubilizing Serratia spp. and plant growth-promoting Pseudomonas spp., while the commercial inoculant contained Azotobacter chroococcum and Pseudomonas fluorescens. Molecular characterization revealed high functional redundancy among the selected strains, with strong potential for phosphate solubilization, colonization, and growth promotion, consistent with overlapping ecological niches. Bioinoculants slightly increased cellulase activity, organic carbon, total nitrogen, and organic matter, whereas chemical fertilization enhanced available phosphorus and exchangeable magnesium while lowering pH. Only boron, pH, phosphorus, and magnesium differed significantly among treatments (p < 0.05). Multivariate analysis linked bioinoculants to greater enzymatic and organic matter variability, and fertilizers to higher C/N ratio and phosphorus. Dasometric traits remained unaffected, indicating treatment-specific substrate effects favoring plant growth, pending field-scale validation. Limitations include the nursery-only setting, six-month evaluation period, and lack of field/biosafety validation.
Food insecurity remains a persistent development challenge in Somalia, where demographic change, rapid urbanisation, limited economic capacity and recurrent climatic shocks interact within a fragile food system. Because food security is multidimensional, this study focuses specifically on domestic food production, measured by the World Bank Food Production Index. Using annual data for 1991–2022, it examines the short- and long-run associations of population growth, urbanisation, real economic activity and rainfall with domestic food production. The study applies the Autoregressive Distributed Lag (ARDL) bounds-testing approach, supplemented by FMOLS, DOLS and CCR estimators. The bounds test confirms a long-run relationship among the variables. Population growth is positively associated with domestic food production in both the short and long run, although the long-run ARDL coefficient is significant only at the 10% level. Urbanisation is negatively and significantly associated with domestic food production, while real GDP is positively associated in the long run. Average annual rainfall is statistically insignificant. The error-correction coefficient indicates that 26.2% of short-run disequilibrium is corrected annually. Overall, the findings suggest that Somalia’s domestic food production is closely associated with demographic, structural and economic conditions, while climate resilience remains important despite the insignificant rainfall coefficient for future policy planning.
Architectural claims in image-based plant-disease recognition usually rest on a single training run per configuration, and disease severity is rarely measured independently of the classifier. We compared four output formulations for joint crop–health recognition on a shared frozen ResNet50 backbone: single-task joint classification, multitask learning with independent heads, multitask fusion, and multitask fusion conditioned on crop and health posteriors. Each was trained with five seeds (twenty models) on 5,235 leaf images of betel vine, brinjal, ladies finger and Malabar spinach. An ordinal severity reference for 96 images was built by independent dual assessment and adjudication, and all models were evaluated on 3,295 unseen potato images. Single-task and both fusion formulations were statistically indistinguishable (joint accuracy 89.36–89.77%); posterior conditioning had no detectable effect (+0.18 points, p=0.475), whereas independent heads underperformed in health-state accuracy (−4.73 points, p=0.019). Seed variation was comparable to formulation differences. Binary labels diverged from adjudicated severity on 18.75% of reference images. On potato, all models discriminated well (ROC-AUC 0.919–0.956) but needed higher thresholds, and formulation rankings reversed between field and repository images. Model outputs, though trained on binary labels, correlated with adjudicated severity (Spearman ρ 0.605–0.715).
Somalia’s drought is conventionally read as a drying and warming trend that erodes food production. This study asks whether the decisive change lies in the variance rather than the mean, and how cereal production responds. Monthly Combined Drought Index values for 73 districts are joined to seasonal production of maize and sorghum, the two staple cereals, for 45 crop growing districts from 2001 to 2025, then analysed with trend and variance tests, panel fixed effects and a threshold regression. Mean drought severity does not deteriorate, and the land surface temperature component of the index shows no trend. Variability changes decisively. The dispersion of the index roughly doubles between the two halves of the record and originates in precipitation rather than temperature. Cereal production responds nonlinearly. Losses accrue six times faster below a growing season index near 0.88 than above it, and moving from favourable into drought conditions removes 54%–61% of district output. Nearly half of all growing seasons fall below that threshold. The hazard is not a drying mean but an acute threshold sensitivity meeting a widening swing in the rains. Early warning should stay rainfall centred but shift from averages to variability and threshold crossing.
Tiger nut (Cyperus esculentus L.) milk processing represents a critical investment opportunity for achieving food system transformation and rural industrialisation in sub-Saharan Africa, yet investment decisions remain hampered by the absence of rigorous financial benchmarks calibrated to current market conditions. This study addresses that gap by evaluating the financial viability and investment risk of a prototype medium-scale tiger nut milk processing enterprise in Techiman, Bono East Region, Ghana. Applying Free Cash Flow (FCF) methodology, net present value (NPV), internal rate of return (IRR), payback period, benefit-cost ratio (BCR), break-even analysis, and sensitivity analysis at a 25% pre-tax discount rate to primary data from 45 processors, 12 suppliers, 18 market vendors, and 10 key informants. The enterprise is financially viable with a strong positive NPV at a 25% discount rate, remaining viable even under the extreme combined adverse scenario. Econometric validation confirms that selling price is the dominant investment risk driver, outweighing raw material cost risk by a factor of approximately eight. Development finance institutions should classify medium-scale tiger nut processing as bankable and prioritise market access over supply-side interventions.
