Method for predicting shedding of cotton buds and bolls

Abstract

The invention provides a method for predicting shedding of cotton buds and bolls, and belongs to the field of agricultural informatization. On the basis of fine simulation of photosynthetic production of cotton leaves and transportation of substances among organs, a cotton three-dimensional growth and development model is constructed, the supply-demand ratio of fruit assimilates is taken as an optimization target, a reasonable threshold value is determined by using a trial-and-error method, feasibility of substance distribution and squaring and boll dynamics of inter-node carbon pool simulation is verified, the occurrence time and position of squaring and boll falling are accurately predicted, and finally accurate prediction of the cotton yield is promoted. According to the method, the physiological mechanism of squaring and boll falling can be quantified, accurate prediction of the time, the position and the number of cotton squaring and boll falling is realized, and an informatization technical means is provided for accurate prediction of cotton yield formation in different planting modes.

Description

technical field [0001] The invention belongs to the field of agricultural informatization, and in particular relates to a method for predicting the shedding of cotton buds and bolls. Background technique [0002] Bud shedding is a key problem in the cotton production process, which directly restricts the improvement of cotton yield and quality. The bud shedding rate of upland cotton is as high as 60% to 70% all the year round, and even reaches 90% under extreme weather conditions. Therefore, it is of great significance to increase the number of bolls per mu and increase the yield of cotton by keeping buds, increasing bolls and reducing shedding. According to the research results of scholars at home and abroad, under the combined effects of light, temperature, water, and nutrients, the source-sink relationship of assimilates is uncoordinated, which is the main reason for the shedding of bud bells (Guinn, 1982a; Zhang Lizhen et al., 2005). For example, Guinn's research shows...

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Problems solved by technology

[0004] At present, researchers mainly calibrate the parameters of the bell shedding model by analyzing the cultivation test data of many years and multiple points. This method is time-consuming, labor-intensive, costly and has many parameters, and it is difficult to form a prediction method with both practicability and accuracy.

In addition, canopy light distribution is a key factor in determining the supply of cotton bud assimilates. The traditional crop model uses a one-dimensional light distribution algorithm, which is difficult to accurately estimate leaf-scale light interception. Therefore, it is urgent to use high-resolution 3D canopy structure model and robust bud-boll shedding model to provide technical support for the prediction of cotton bud-boll shedding

[0005] Pan Xuebiao (1999) established the simulation module of bud shedding in the cotton growth and development model COTGROW through the method of combining expert experience parameterization and mechanism model. However, there are many parameters and it is difficult to parameterize. Zhang Lizhen et al. (2003) used the modeling method of escalator boxcar train to realize the simulation of the growth and shedding process of bell bells based on the balance between the supply and demand of assimilates at the plant scale. The method is organic, simple and practical, but does not take into account environmental effects and spatial distribution

Li Yu et al. (2013) comprehensively considered the effects of temperature and photosynthetically active radiation on the growth and development of cotton, based on the cumulative temperature and light effect index TSE, determined the simulation of the impact of nitrogen and waterlogging on cotton bud and boll shedding, and introduced the genetic benefits of varieties, Accurate prediction of the number of bolls per plant and shedding rate of cotton can be achieved, but this method requires a large amount of field test data for parameter correction, and does not consider the nodes where the bolls fall off

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