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    Climate thresholds and yield elasticity of durian, mangosteen, and coffee under hydroclimatic variability in Thailand
    (2026-08-01)
    Ansari, Kutubuddin
    ;
    Tanır Kayıkçı, Emine
    ;
    Jamjareegulgarn, Punyawi
    This study examines the empirical relationships between hydroclimatic variability and the yields of durian, mangosteen, and coffee across Thailand using meteorological observations from 56 stations and provincial level yield statistics for 2008 to 2024. An integrated statistical framework was applied, including exploratory correlation analysis, nonlinear quadratic additive modelling, lagged climate response analysis, elasticity estimation, model comparison, mangosteen regional sensitivity analysis, and a Climate Risk Index (CRI). The results show clear spatial gradients in temperature, humidity, and precipitation across the six agroclimatic regions of Thailand, broadly corresponding to regional crop productivity patterns. Annual anomaly analysis indicates that warm and humid years are generally associated with higher durian and mangosteen yields, whereas excessive rainfall and warming are associated with reduced coffee productivity. Pairwise linear correlations between annual climate variables and crop yields are generally weak, suggesting that simple linear models may not fully capture crop climate relationships. Nonlinear modelling indicates approximate empirical temperature turning points near 28.0°C for durian, 27.3°C for mangosteen, and 26.6°C for coffee, with humidity related turning points around 76–78%. Lagged climate response models suggest that multi-year hydroclimatic conditions may influence perennial crop productivity, particularly humidity for durian, precipitation for coffee, and temperature and precipitation for mangosteen. The mangosteen sensitivity analysis shows that humidity and precipitation thresholds are affected by regional composition, especially when marginal production regions are included. The CRI formulation under the equal weight, mangosteen shows the highest rainfall related climate risk signal, while coffee is more sensitive to temperature related risk and durian shows moderate vulnerability to prolonged humid and wet conditions.
      1
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    Physical and Functional Properties of Spent Coffee Ground after Various Drying Techniques
    (2025-01-01)
    Maiyah, Nur
    ;
    Auenchitr, Yatima
    ;
    Supapvanich, Suriyan
    ;
    Sirijariyawat, Arpassorn
    ;
    Ingkasupart, Pajaree
    Spent coffee ground (SCG) consists of impact compositions and functional ingredients that could be applied to food products. Drying techniques and conditions had an impact on the qualities of SCG. The aim of this research was to investigate the chemical compositions of roasted coffee ground (CG) and SCG from Arabica (A) and Robusta (R) coffee, as well as the physical and functional properties of SCG before and after different drying using hot air tray dryer and freeze dryer. It was found that SCG had moisture and carbohydrate content of 64.79–65.87% wb and 16.55 18.57%. Arabica spent coffee ground (ASCG) had more lipid and less protein, caffeine and phenolic content than that of Robusta spent coffee ground (RSCG). The dried SCG had moisture content lower than 5% for all drying conditions. Moreover, the dried SCG had Aw, solubility, and rehydration in a range of 0.25-0.55, 6.02-6.92%, and 140.67-180.37%, respectively. Freeze-dried SCG retained more functional ingredients than tray-dried SCG across all drying conditions.
      7
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    Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning
    (2023-03-01)
    Ruttanadech, Nuttapong
    ;
    Phetpan, Kittisak
    ;
    Srisang, Naruebodee
    ;
    Srisang, Siriwan
    ;
    Chungcharoen, Thatchapol
    Near-infrared (NIR) spectral-based classification of Aspergillus ochraceous contamination in the Robusta green coffee bean was investigated. Six different learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (KNN), decision tree (Tree), Naive Bayes (NB), and quadratic discriminant analysis (QDA), were applied for the investigating purpose. Four classes of fungal contamination on coffee beans, non-fungal contaminated beans on day 1 and day 3 (NCB-D1 and NCB-D3) and fungal contaminated beans on day 1 and day 3 (CB-D1 and CB-D3), were set for the classification intention. Based on the 6 learning algorithms, the Tree approach was optimal, displaying a training accuracy of 97.5%. As proven by the testing dataset, the classification accuracy of the Tree was also at 97.5%. With this number, the Tree could correctly classify 100% between the contaminated and non-contaminated coffee beans. These findings exhibit the potential of the NIR spectroscopy accompanied by machine learning for the early detection of fungal contamination in green coffee beans.
      5
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    Application of a new bio-formulation of chaetomium cupreum for biocontrol of colletotrichum gloeosporioides causing coffee anthracnose on arabica variety in Laos
    (2017-01-01)
    Vilavong, Somlit
    ;
    Soytong, Kasem
    The anthracnose pathogen was isolated from coffee leave and bean symptoms. Morphological and molecular phylogenetic data confirmed the species as Colletotrichum gloeosporioides. The pathogenicity of the isolate was also confirmed by detached leaf method which inoculated the virulent isolate into coffee leaves. The crude extracts with hexane, ethyl acetate and methanol solvents from Chaetomium cupreum CC3003 resulted significantly inhibited C. gloeosporioides that the ED<inf>50</inf> values of 13, 11 and 28 ppm, respectively. The bioactive substances of C. cupreum CC3003 expressed antifungal activity against C. gloeosporioides as can be seen in the abnormal appearance of spores. A powder bio-formulation of C. cupreum significantly resulted to reduce anthracnose disease of 54.77 %. The application of nano-rotiorinol, nano-trichotoxin and a spore suspension of C. cupreum reduced anthracnose incidence of 46.23, 42.71 and 18.59 %, respectively while the inoculated control had high anthracnose disease. The application of bio-formulation of C. cupreum in powder form, nano-rotiorinol, and nano-trichotoxin to reduce coffee anthracnose was reported for the first time in Lao PDR.