CHR: An Integrated Software Tool for chilling requirements

Background Bud dormancy in deciduous fruit trees enables plants to survive cold weather. The buds adopt dormant state and resume growth after satisfying the chilling requirements. Chilling requirements play a key role in owering time. So far, several chilling models, including ≤ 7.2 °C model, the 0–7.2 °C model, Utah model, and Dynamic Model, have been developed; however, it is still time-consuming to determine the chilling requirements employing any model. This calls for ecient tools that can analyze data. Results In this study, we developed novel software Chilling and Heat Requirement (CHR), by exibly integrating data conversions, model selection, calculations, statistical analysis, and plotting.


Background
Bud dormancy in deciduous fruit trees enables plants to survive cold weather. The buds adopt dormant state and resume growth after satisfying the chilling requirements. Chilling requirements play a key role in owering time. So far, several chilling models, including ≤ 7.2 °C model, the 0-7.2 °C model, Utah model, and Dynamic Model, have been developed; however, it is still time-consuming to determine the chilling requirements employing any model. This calls for e cient tools that can analyze data.

Results
In this study, we developed novel software Chilling and Heat Requirement (CHR), by exibly integrating data conversions, model selection, calculations, statistical analysis, and plotting.

Conclusion
CHR is a tool for chilling requirements estimation, which will be very useful to researchers. It is very simple, easy, and user-friendly.

Background
The chilling requirements (CR) [1] refer to the value of low temperature required for deciduous fruit trees to overcome their natural dormancy. It is an important quantitative index for measuring their dormant and dormant release characteristics. Only a deciduous fruit tree that satis es a certain level of chilling requirements can break the dormancy and carry out a normal series of growth and development; otherwise, the germination, owering, ower bud differentiation, fruit development, and yield will be adversely affected [2]. With the environmental pollution and global warming, the chilling requirements of fruit trees is affected, which leads to early germination, poor fruit development, small fruit volume and uneven ripening time, resulting in economic loss to farmers [3][4][5][6].
There are several models for calculating the chilling requirements. Based on their regularity, the models can be roughly divided into two types: distribution functions (type I) and dynamic models (type II). In type I, Weinberger [7] rst proposed the low-temperature hour model (0-7.2), which is still widely used today.
Some models have a smaller range of low temperature conversion than 0-7.2, so the calculation results conform well to the natural law. Due to the difference in temperature conversion range, some models such as North Carolina Model (NC) [8] and Positive Utah Model [9] are more effective for speci c regions [10]. The dynamic model is different from other models in that it calculates chilling requirements through dynamic two-step processes [11][12][13]. In this model, since more factors are considered than other models, and the repeatability is excellent in both cold and warm regions, it is regarded as the best model [6].
However, due to its complexity, its application is limited.
At present, there is no independent interface visualization tool for the calculation of cooling demand. The traditional treatment method makes the statistics di cult for researchers and fruit growers. The developed CHR is an entirely independent tool. It offers robust statistical results for different models, only by providing temperature data. CHR carries out simple analysis and drawing functions to implement the rules of each model.

Implementation
We show an overview of the CHR work ow in Fig. 1. Firstly, the data are saved in .txt format, and the saved data can be used to calculate daily maximum, minimum, and average values, and the trend of change can be observed through the drawing function. The data can also be used to calculate the chilling requirements and GDH, and the results can be further used to draw or evaluate the model. 0-7.2, Utah, GDH, and dynamic models are compiled with python, and designed by pyqt5. The drawing tools are implemented by Matplotlib and Seaborn.

Variability setting of model parameters
In order to achieve the convenience of the model parameter setting, the model parameter is set to the default parameters and assigned to a variable. When researchers change parameters, the value of a variable is also altered, and the rationality of parameter settings is veri ed before the execution of the program.

Creation of custom model tool
In order to facilitate the researchers to set up a unique model for the region and to realize type I model, Forcing model GDH (Growing Degree Hours) was proposed by Anderson [15] and Luedeling [13] developed it further. It considers hourly temperatures (Th), as a function of a base (Tb), an optimum (Tu), and a critical (Tc) temperature. The underlying assumption is that heat accumulates, when temperatures range between Tb and Tc, with maximum accumulation at Tu. For temperatures between Tb and Tu, the corresponding equation is:

Data preparation
The temperature recorder continuously monitors the changes in temperature, which results in the recording of multiple temperatures in an hour. The tool averages data recorded per hour, helping researchers quickly obtain hourly data.

Chilling and Forcing model
The calculation method of the type I model is the same as that of the forced model. After inputting the data, the researcher obtains three calculation results, namely, chilling accumulation per hour, for the whole day, and cumulative from the rst day. The calculation method of the dynamic model is different from other models, and the result of calculation gives the amount of stable productions. Researchers can further analyze the data obtained.

Model analysis and Drawing function
The choice of the chilling model in the planting area is very important. If the model is not appropriate, it will not help fruit growers to make correct decisions. The comparative analysis of the CHR model is based on the principle of the linear regression model. This function can be used to linearize the data (y = kX + b), determine 95% con dence intervals, observe the outliers, and select the appropriate model.
The tool also offers simple temperature data analysis and mapping. Researchers and fruit growers can use these two functions to calculate the maximum, minimum, and average values of the daily temperature, drawing function includes trend of the selected model maximum, minimum, and average values of the daily.

Discussion
The traditional calculation method of chilling accumulations is cumbersome. It also does not work well while analyzing complex models, such as dynamics. This led researchers to spend more time on model calculations rather than analysis. CHR tool is the rst entirely independent interface visualization tool, which is easy to operate, set parameters, and does not require knowledge of the programming language.
will be further improved in future development.

Conclusions
Here we report CHR, a tool for chilling requirements estimation, which will be very useful to researchers. It is very simple, easy, and user-friendly. All sample data, software instructions and software can be obtained from https://github.com/Chilling-requirements/Chilling-software/releases.

Availability of data and materials
The tool is open source and available from Github.
Ethics approval and consent to participate Not applicable.

Consent for publication
Not applicable. Change of the max and min temperature

Supplementary Files
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