ASHS 2024 Conference

Accurate prediction of cocoa yields is critical for farmers, governments, and industry as it influences logistics and supports decision-making. In tropical agriculture, there has been a recent trend toward integrating sensor technology, data science, and machine learning to enhance management and boost crop production. However, cocoa yield prediction models rely on the quality and availability of public datasets and genetic differences among cocoa genotypes. Moreover, current cocoa models require technical skills for satellite image processing or a significant investment in sensors and software. Different statistical models in cocoa predict yield independently of physiological processes or disease pressure. Therefore, we propose a mechanistic model that uses historical yield and weather data from 2010 to 2023 and the in-field sampling from 61 orchard plots from four farms in Guayas, Ecuador. Time series measures of cocoa pods and disease incidence per tree in the plots were recorded for the 2023 and 2024 growing seasons. Cocoa pod counts and diseased pods, as well as tree photographs for biomass calculation, were recorded using a customized mobile application. Ecuador´s cocoa production in this location has a bimodal annual distribution, with the highest peak following the start of the rainy season. Moniliasis disease also presented the highest incidence within the next two months of precipitaion peaks. Several varieties of cocoa are grown in Ecuador, but production is dominated by two main groups: Fine cocoa or national flavor and CCN-51. The national type of cocoa is characterized by its unique flavor profile. However, it is prone to diseases and has a lower yield. To overcome these challenges, our study aims to develop a machine learning-based model geared towards Ecuador's distinct national type cocoa varieties, taking into account local climate patterns, soil characteristics, biomass, and direct cocoa pod field counting. The analysis reveals that cocoa yield variability is affected mainly by moniliasis disease incidence, tree biomass, and environmental factors such as temperature, solar radiation, and precipitation. In contrast, soil texture, pH, and electrical conductivity had minor variations and a negligible effect on yield changes. The proposed model was compared with other machine learning algorithms based on Mean Absolute Error and Mean Square Error. The validation phase, employing the Mean Absolute Percentage Error, indicates our model's substantial predictive accuracy with a confidence interval of 73.4 percent at the 0.1 significance level and confirms the model's effectiveness in forecasting cocoa yields under Ecuadorian conditions.

The maturity stage at harvest influences the postharvest quality and storage life of fruit. Harvesting jackfruit (Artocarpus heterophyllus Lam.) at the optimum maturity stage is more important due to the very large size of the fruit. Therefore, the present investigation was conducted to determine the impact of four different maturity stages on physicochemical parameters and fruit-softening enzymes in two jackfruit genotypes (Accession 242 and Accession 341). Fruit for the initial three stages were harvested at 10 days intervals commencing 90 days after flowering (DAF), while fruit for the final stage were harvested based on traditional maturity parameters (142 and 153 DAF) in Accession 341 and Accession 242 respectively. Results indicated an increase in fruit size, seed and bulb weight with the advancement of fruit maturity in both accessions. Moreover, there was a concurrent decline in spine density and an increase in spine flatness with the progression of fruit maturity. Whilst pulp percentage increased, there was a decrease in rag and core percentage at advanced maturity stages. However, peel and seed percentage were not influenced significantly by the advancement of fruit maturity. Additionally, there was a decrease in L* value with a concomitant increase in a*, b*, and C* values of the bulb with the advancement of fruit maturity. In both accessions, bulb firmness, and moisture content were highest in fruit harvested at 90 DAF. The decline in bulb firmness was associated with higher activities of polygalacturonase, pectin methyl esterase, cellulase, and pectate lyase. In both accessions, soluble solid content (SSC), DPPH radical scavenging activity, total flavonoids, and ascorbic acid content were higher in fruit harvested during the last stages of maturity while titratable acidity and total phenolics content were comparatively lower in advanced maturity stages. These findings highlight that the earlier stages (90 and 120 DAF) are suitable as meat alternatives due to better texture, while the most favourable stages for fresh consumption are 142 ± 4.7 and 153 ± 5.7 DAF in ‘Accession 341’ and ‘Accession 242’ due to better colour, higher pulp percentage, higher SSC and antioxidant activity, respectively.

Papaya (Carica papaya) is an herbaceous perennial plant native to the tropical Americas. It thrives in tropical and subtropical climates. While the United States is the world’s largest papaya importer, it also cultivates a modest number of papayas, primarily in Hawaii, California, Texas, and Florida. The subtropical climate in Mississippi offers favorable conditions for papaya cultivation. However, frosts in late fall or early spring could pose threats to both plants and fruits. Appropriate cultivar selection and management strategies are critical for successful papaya production in MS. Currently, limited information is available on growing papayas in MS. The objective of this study was to investigate the potential of growing papaya as an alternative crop for specialty crop growers in MS. Preliminary data suggest that papaya plants can grow well in Mississippi. Different cultivars exhibited variations in plant size and vigor. Plants produced papaya fruits with variations in fruit number, size, and shape, as well as timing to fruiting among different cultivars. However, the relatively shorter growing season in MS compared to tropical climates resulted in insufficient time for most fruits to ripen before the onset of frost in late fall. Further research will involve evaluating additional cultivars, adjusting production schedules, employing season extension techniques, and assessing economic feasibility.