Accurate dry matter determination (DM) in Hass avocados is vital for optimal harvesting and ensuring fruit quality. Predictive models based on NIRS need to capture fruit DM gradient. This work aimed to determine the DM content in Hass avocado whole by NIRS scanning different fruit zones. Spectra were recorded for each zone of the fruit: peduncle (P), equator (E), and base (B). The calibration and validation included fruit from different orchards in two harvest cycles. The results show a DM gradient within the fruit: 24.47% (E), 24.68% (B), and 24.79% (P). The DM gradient was observed within the spectra using the RMSi (root mean square) criterion and PCA. The results show that at least one spectrum per fruit zone was needed to represent the variability within the fruit. The performances of the calibration using the whole set of data were R²: 0.74 and standard error of cross-validation (SECV) = 1.18%. In the validation stage using independent validation sets, the models showed similar performance (R²: 0.75, SECV 1.15%) with low values of the standard error of prediction (SEP): 1.62%. These results demonstrate the potential of near-infrared spectroscopy for high-throughput sorting of avocados based on their commercial quality.

Hass avocado quality varies by origin, season, and production practices. However, there is a lack of methodologies to guarantee that fruit reaching the market has consistent quality. The aim of this work was to identify predictive markers for quality management. Fruit samples produced under different nutrient management, elevation, date-to-harvest, and growth cycle conditions were analyzed. Dry matter, oil content, internal disorders, sensory attributes, minerals, and fatty acids were evaluated as quality variables. The results highlighted soil and weather differences among orchards. Nutrient management practices based on index balancing in some samples increased both productivity and fruit size. High variability was observed in the dry matter related to the age of the fruit at harvest. Ripening heterogeneity was very large in low-elevation orchards where the fruit was picked relatively early. High flesh mineral contents delayed fruit ripening. At low growing temperatures, more oleic and linoleic acids were present in fruits. The sensory texture and taste descriptors were affected by the fruit age and related to the flesh composition. Logistic, PLS-DA, and biplot models effectively represented the variabilities in the ripening pattern, composition, and sensory profile of avocado fruits and allowed the samples to be grouped according to the internal fruit quality.

El cultivo de aguacate Hass se ha desarrollado de forma exponencial en varias regiones de Colombia, lo que ha contribuido al incremento de los ingresos de pequeños productores y al aumento considerable del PIB del país. La presente publicación, denominada Modelo productivo para el cultivo del aguacate Hass en Antioquia, constituye un producto de la concertación regional entre productores, asistentes técnicos o extensionistas agropecuarios, comercializadores e investigadores, que integra los resultados de investigación nacional e internacional, con las “prácticas tradicionales sobresalientes” identificadas en las diferentes subregiones productivas. Tiene como objetivo principal unificar criterios y estrategias de manejo sostenible del cultivo, con criterios de calidad y sostenibilidad económica y ambiental, que garantice bienestar social en los territorios. Esta publicación se materializa gracias al apoyo financiero de la Gobernación de Antioquia, a través de su Secretaría de Agricultura y Desarrollo Rural y al esfuerzo colaborativo de varios actores relacionados con la cadena productiva del aguacate Hass destinado al mercado internacional, bajo la moderación y liderazgo del Centro de Investigación La Selva de AGROSAVIA.

Understanding how climatic variability shapes the reproductive behavior of perennial crops is essential for improving their adaptation to tropical mountain environments. This study examined the influence of altitudinal and thermal gradients on flowering synchrony in ‘Hass’ avocado (Persea americana Mill.) cultivated across the tropical Andes of Colombia. Climatic variables and phenological stages were monitored across three elevations (2056, 2212, and 2338 m.a.s.l.) during two production cycles. Principal component, confirmatory factor, and circular statistical analyses were applied to integrate multivariate climatic structure with temporal flowering patterns. Results revealed that temperature was the main climatic driver of phenological variability, with significant differences among altitudes. The study revealed an altitudinal thermal–hydric gradient structuring distinct microclimates between 2050 and 2350 m.a.s.l., which determine the synchrony and rate of ‘Hass’ avocado phenological processes. Anthesis was the most environmentally sensitive phase, showing greater stability at intermediate elevations (~2200 m). Multivariate (PCA, CFA, ANOVA) and circular analyses confirmed that accumulated temperature (GDD) effectively predicts phenological progression, defining an optimal altitudinal range for synchrony and productivity in high Andean temperate zones.

Knowing, with reasonable accuracy, the dry matter (DM) content of Hass avocado fruit will help determine when the fruit must be harvested. The reliability of predictive models based on near infrared spectra for DM quantification depends on the ability of the spectra to be representative of the DM gradient within a whole fruit. The aim of this work was to develop a methodology to determine the optimum number of spectra to develop a robust model for DM content quantification. Three spectra were recorded for each zone of the intact fruit: peduncle, equator, and base. Each scanning point was sampled, and the DM content was determined using oven drying. Two-way ANOVA confirmed the DM gradient within the whole fruit. This gradient was observed within spectra using the RMS (root mean square) criterion and PCA. The PLS models showed that at least one spectrum per zone could be enough to construct an efficient and robust model for dry matter quantification.