The coffee cultivar Catuaí is among the most successful cultivars in Brazilian agriculture; it has been on the market for more than 40 years. It was obtained by Dr. Alcides Carvalho, a researcher of the Instituto Agronômico de Campinas (IAC), from the cross between ‘Caturra’ and ‘Mundo Novo’ carried out in 1949 for the purpose of joining plant vigor with small plant size. Our aim was to report the activities that culminated in the recommendation of 16 lines of ‘Catuaí’, consisting of eight lines with red fruit and eight with yellow fruit, analyzing the data of several experiments. The decision regarding what to recommend was made in the F_1:2 generation, based on two harvests. It became clear that Dr. Alcides should be taken as an example by all breeders, above all in his persistence, scientific rigor, and belief that farmers can be an important ally of breeders.

Coffee genetic improvement programs have been evolving very quickly, with frequent launches of new cultivars. The adoption of these new genetic materials by rural producers requires knowledge of agronomic performance in different production systems. Thus, this research aimed to evaluate the agronomic performance of irrigated and rainfed Arabica coffee (Coffea arabica L.) cultivars in the Cerrado Mineiro region. Evaluations were conducted in experimental fields across 22 farms of Arabica coffee producers, and 11 used an irrigated production system and 11 used a rainfed system. Twelve cultivars were evaluated as follows: Catuaí Vermelho IAC 144, Bourbon Amarelo IAC J10, Topázio MG 1190, MGS Epamig 1194, Catiguá MG2, MGS Catiguá 3, MGS Ametista, Pau Brasil MG1, MGS Paraíso 2, MGS Aranãs, Sarchimor MG 8840, and IAC 125 RN. Based on grain yield, processing yield, seed density, grain size, and cup quality, agronomic performance evaluations were conducted annually for the 2019, 2020, 2021, and 2022 harvests. The results showed that the grain yield was higher in the irrigated system compared to the rainfed system. In irrigated fields, the average increases in grain yield were 38%. Irrigation improved the performance of the cultivars in terms of processing yield, although it reduced cup quality. MGS Paraíso 2 cultivar showed the best productive performance, with an average of over four harvests of 52 and 42 bags ha⁻¹ (1 bag = 60 kg) in irrigated and rainfed systems, respectively. The cultivars least responsive to irrigation were IAC 125 RN, MGS Catiguá 3, MGS Ametista, and MGS Paraíso 2, with grain yield increases of 24%, 26%, 27%, and 28%, respectively. The most responsive cultivars were MGS EPAMIG 1194, Sarchimor MG 8840, and Pau Brasil MG1, with grain yield increases of 33%, 35%, and 39%, respectively. The agronomic performance results of coffee cultivars in irrigated and rainfed production systems will allow rural producers to adopt cultivars that are more suitable for the Cerrado Mineiro region.

Experiments with progenies of perennial species such as coffee are generally affected by the heterogeneity of residual variances between information repeatedly collected in space and time on the same individual. In this study, we propose an index that considers the individual heritability of multiple traits for progeny selection and evaluate the applicability of this index in comparison with other indices in a real dataset. Data from 30 coffee genotypes in the F_4:5 generation were used to obtain the individual heritability values ($h_i^2$) of progenies that were subjected to factorial analysis to obtain the scores and construct a scatter plot, where graphical analysis (GA) was applied. Genetic gains were obtained for productivity and resistance to Cercospora leaf spot using GA. The best performance among the strategies (−12.11%) was obtained using GA for resistance to Cercospora leaf spot, which has low heritability—contrary to the result obtained using the commonly used index based on the sum of Mulamba and Mock ranks. The GA approach allows an assertive selection to minimize the effects of heterogeneity between seasons, and greater genetic gains are obtained. Its use as a tool for the selection of perennial plant progenies based on multiple characters is promising.