Potato is the fourth staple crop in China after wheat, maize and rice. The agro-pastoral ecotone (APE) in North China is a main region for potato production. However, potato yield has been seriously constrained by water shortages because of low precipitation and highly variable precipitation patterns during the growing season in this area. In this study, the Agricultural Production Systems Simulator (APSIM) model was used to simulate potato water-limited yield and historical years were divided into different water-temperature year types to optimize the optimal planting period (OPP) and cultivar of potato. The results showed that the potato yield varied in different water-temperature year types. Fast-developing cultivar Favorita could obtain the highest yield in most places and water-temperature year types due to its relatively short length of tuber formation stage. In this study, we suggest changing the planting date according to the water-temperature year type, which offers a new way to adapt to a highly variable climate. However, our method should be adopted carefully because we only considered climate factors; other agronomic management practices (adjusting planting density, plastic film mulch, conservation tillage etc.) also have a great effect on planting date and cultivar selection, which should be further investigated in the future.

Generic parameter calibration for crop growth models is a very important step in model use. However, studies of the effect of sample size and sampling methods on the calibration and validation of genotypic parameters have seldom been conducted. Scientists commonly apply the hold-out (HO) method, by default, to deal with samples for calibration and validation in the practice of model use. In this paper, we applied the hold-out, cross-validation (CA), and bootstrapping (BS) methods with different sample sizes to analyze the influence of sampling methods and sample size on the final calibration results of genotypic parameters. The results showed that, (1) overall, CA and BS performed better than HO at most observation stations. However, there was great variability in the calibration and validation results obtained from the three methods. (2) Because of data quality differences, we could not conclude that the more samples there were, the greater the validation accuracy of the three methods. (3) The CV of the genotypic parameter values for the three methods and sample sizes varied greatly. Thus, when genotypic parameter calibration is performed, both sampling methods and sample size should be considered.