Parameter estimation for complex process models used in agronomy or the environmental sciences is important, because it is a major determinant of model predictive power, and difficult, because the models and associated data are complex. Statistics provides guidance for parameter estimation under various assumptions concerning model error, but it is hard to know which assumptions are most acceptable for these models. We therefore propose a collection of parameter estimation methods. All are based on weighted least squares, but different assumptions lead to different weights. The methods allow one to fit simultaneously several different response variables. One can assume that all errors are independent or on the contrary are correlated. One can assume that model error has expectation zero or not. A software package called OptimiSTICS has been developed, that allows one to implement all of the proposed methods with the STICS crop-soil model. The software can in addition treat the case where some parameters are genotype specific while others are common to all genotypes. The software can also automatically do several sequential stages of parameter estimation. An example is presented, which shows the information that can be obtained, and the conclusions drawn, from comparing the different estimation methods.