Wheat growth period characteristic parameter evaluation method based on R language

Abstract

The invention provides a wheat growth period characteristic parameter evaluation method based on R language. The wheat growth period characteristic parameter evaluation method based on R language is characterized in that recompiling a wheat growth analogy model by means of R language; setting a parameter initial value through Latin hypercube, and obtaining prior probability distribution of wheat variety characteristic parameters through operation; according to the initial value and prior probability distribution, selecting the candidate parameters; calculating the probability density function of the growth period, and determining whether or not to receive the new parameter; and then finally obtaining posterior probability density distribution of each characteristic parameter of varieties. The wheat growth period characteristic parameter evaluation method based on R language utilizes the non-linear least square method to improve the sampling efficiency to avoid the parameter falling into local optimum; based on the Bayesian theory framework, prior distribution of parameters are effectively considered; the error correlation problem between stations can be solved through conversion of the original data, so that the parameter debugging result can be accurate and efficient; and the wheat growth period characteristic parameter evaluation method based on R language has general applicability in characteristic parameter evaluation of the same wheat variety.

Description

technical field [0001] The invention belongs to the field of agricultural information technology, in particular to a method for estimating characteristic parameters of wheat growth period based on R language. Background technique [0002] Global climate change has been an indisputable fact since the Industrial Revolution. Climate change on a global scale not only affects the environment on which humans depend, but also poses severe challenges to agricultural production. With the continuous deepening of the research on global climate change, the research on the impact of climate change on crop growth and yield formation has also begun to receive extensive attention. Under the scenario of future climate change, timely and accurate prediction of crop growth is of great significance for guiding and regulating agricultural production, improving management level, and formulating appropriate grain production policies. [0003] The crop production system is affected by factors suc...

AI Technical Summary

Problems solved by technology

The parameters are closely related to the crop model, and the parameter values ​​in the model are generally difficult to obtain directly, and due to the different methods of obtaining the parameter values ​​and the different input data, there is a large uncertainty in the simulation results, which increases the user's decision-making process based on the model. uncertainty

However, most of the existing studies on the uncertainty of crop simulation models have mixed the three sources of uncertainty, namely parameter values, input variables and model formulas, and have not studied the influence of parameter uncertainty on the uncertainty of simulation results. influences

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