The growth and yield of crops within a farm largely vary among fields. Farms are increasing in size by acquiring smaller land parcels from different farmers who have different management strategies. As a result, between-field variability increases and understanding such variability is a necessity for precision farming. New data analysis techniques are needed in this context, especially given the trend that more farms are collecting more data. Therefore, this study has the objective to provide a data analysis methodology to analyze within-year variability and identify year-independent factors that influence growth. As a second objective, we applied this novel methodology to a case study, where we analyzed potato growth data of four successive years of a farm in the south of the Netherlands. The methodology consists of three main steps: (1) describing growth using mixed models, (2) clustering and explaining growth by correlating the clusters to (a) yield, (b) other plant characteristics and (c) to defining, limiting and reducing variables, and (3) predicting growth by automatically selecting a regression model. By applying our method on the potato growth data, we obtained the following results. The main results of the work are: (1) the estimated growth curves of the stems, haulm and tubers explain the between-field variability in growth well very well (R2 of 0.85, 0.74 and 0.89, respectively), (2) clusters with a stem length between 110 and 130 cm have the highest average yield, (3) deeper groundwater level and sugar beet or grass as previously cultivated crop positively influence growth, and (4) N and K fertilization must be adjusted for optimal growth. Concluding, this study responds to the quest for new data-based methods for sustainable intensification, and is the first to explicitly analyze and explain differences in crop growth between fields in practice. In addition, clear management advice could be provided, showing the scientific and practical potential of our methodology.