Abstract: Abstract: As the inputs of the crop growth model increased, based on data of multiple sites, weather, and soil, and especially when dealing with massive regional data, the response time of the model gets longer. After a parallel computation scheme of cloud computing was selected in this paper, considering the large amount of weather data, an algorithm of crop growth model based on Cloud Computing was proposed to improve parallel computation speed and response time of the crop growth model. First, the authors analyzed the Crop growth model and data dependence relationships among sub-models, and then summarized different parallel computation schemes. From a system constitution perspective, the crop growth model included model description, model structure, model algorithm, and forcing data. Complex data dependence relations between sub-models and among computing units in the sub-models comprised independency, synchronous dependency, self-reliance, and interdependency. Parallel computation was grouped into data-intensive computing and computing-intensive computing, according to characteristics of the calculation. The former was suitable for computation tasks with large amount of data and simple computing relations, while the latter was suitable for computation tasks with little amount of data and complex computing relations. Second, a scheme of crop growth model based on Cloud Computing was designed on the basis of Hadoop, which is an open-source software of Cloud Computing infrastructure. The MapReduce parallel computation scheme of Crop growth model assumption was that computing tasks of all sub-models in a regional point of the same crop were viewed as a computing job, and a number of computing nodes completed crop growth process computing of multiple regional points. Hence, the granularity of MapReduce parallel computation was a regional point crop, and a computing task of crop growth model could be broken down into multiple sub-computing tasks that executed on different nodes in parallel. The object-oriented approach was employed to design different sub-m. Third, taking Wheat Grow, a wheat growth model from the National Engineering and Technology Center for Information Agriculture, as the testing target, the effectiveness of this scheme was verified in a real Cloud Computing environment. Exemplified by the development stage sub-model, according to contrast research using data-intensive parallel computation methods and computing-intensive parallel computation methods, data-intensive parallel computation methods had better advantages of performance. Therefore, when dealing with crop growth model which had complex data dependence relations, if there appeared more regional data points, the data-intensive parallel computation method was more reasonable to be employed. The advantages of MapReduce extendibility was further reflected based on the more regional data points and the added calculating nodes. When regional points data of crop was fixed, the test line of program runtime fell below the proportional line and increasing tendency gradually became smaller. It also showed that MapReduce had good extendibility. Hadoop was not suitable for processing a small amount of data, and a pseudo-distributed environment was not suitable for the calculation, but pseudo-distributed environment provided convenience for program development. Finally, the authors suggested that this thesis had fixed guidance on regional applications of crop growth mode, and it could achieve both increasing production and income of regional crops and provide reference to promote the development of the crop growth model and the digital agriculture development. Its application prospect was very wide.