A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

Coffee plays a key role in the generation of rural employment in Colombia.More than 785,000 workers are directly employed in this activity,which represents the 26%of all jobs in the agricultural sector.Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities,and resources(number of workers,required infrastructures),anticipating negotiations,estimating,price,and foreseeing losses of coffee production in a specific territory.These important processes can be affected by several factors that are not easy to predict(e.g.,weather variability,diseases,or plagues.).In this paper,we propose a non-destructive time series model,based on weather and crop management information,that estimate coffee production allowing coffee growers to improve their management of agricultural activities such as flowering calendars,harvesting seasons,definition of irrigation methods,nutrition calendars,and programming the times of concentration of production to define the amount of personnel needed for harvesting.The combination of time series and machine learning algorithms based on regression trees(XGBOOST,TR and RF)provides very positive results for the test dataset collected in real conditions for more than a year.The best results were obtained by the XGBOOST model(MAE=0.03;RMSE=0.01),and a difference of approximately 0.57%absolute to the main harvest of 2018.

Effects of the Orientation of the Mountainside, Altitude and Varieties on the Quality of the Coffee Beverage from the “Matas de Minas” Region, Brazilian Southeast

Based upon qualitative parameters experiments, this study aims to investigate how the elements of the environment, where the coffee is produced, contribute to the final quality of the product. For the analyses, it was used approximately one kilogram of coffee cherry samples collected in 14 municipalities previously chosen on the East side of the Minas Gerais State, Brazil. The coffee cherry samples were collected and analyzed for each of the two varieties in four levels of altitude for each exposure side of the mountain in relation to the Sun. The quality of the coffee was evaluated through the analysis of its physical characteristics and sensory analysis, popularly known as "Test of drink or Cupping" carried out by three tasters that belonging to the group of Q-Graders, according to the rules of national and international competitions of the Brazilian Association of Special Coffees (BSCA). Were performed analysis by means descriptive statistics, analysis of variance and multivariate analysis, all of them aiming to study the individual sensory characteristics of quality of the coffee beverage from the “Matas de Minas” region. Path coefficient analysis also was carried out for the partition of the phenotypic correlation coefficients into measures of direct and indirect effects, in order to determine the individual sensory characteristics that played a major role in the beverage final score. The results demonstrate that it is not possible to conclusively establish the differences among coffees evaluated with basis on varieties and environmental factors previously cited. It can be concluded that it is not recommended to associate the quality of coffee only to a specific factor whether from the environment or being it a specific of the culture of coffee. However, the cafes that were evaluated had intrinsic quality, which were derived from the specific characteristics of the “Matas de Minas” region where they were grown.