Abstract
The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.
Highlights
Chinese Brassica (Brassica chinensis L. var. parachinensis (Bailey)) has high nutritional value and is a critical vegetable for the Chinese population whose diet consists largely of vegetables in the Guangdong province of China, and Chinese Brassica is one of the highest produced vegetables in Guangdong province [1]
wind speed (WS) and photosynthetic active radiation (Par) showed a similar range in both E1 and E2, in which the majority of WS and Par in E1 were lower than those of E2
Our study focuses on simulating the canopy temperature of Chinese Brassica using a machine learning algorithm, which has been used to predict biological parameters in agriculture well, such as the crop yield of cotton [44], the leaf chlorophyll content of wheat [60], and the leaf nitrogen content of wheat [61], and the R2 values were over 0.9
Summary
Chinese Brassica (Brassica chinensis L. var. parachinensis (Bailey)) has high nutritional value and is a critical vegetable for the Chinese population whose diet consists largely of vegetables in the Guangdong province of China, and Chinese Brassica is one of the highest produced vegetables in Guangdong province [1]. Chinese Brassica is susceptible to soil water deficiency, which affects its ability to undergo photosynthesis and stomatal movements and eventually leads to a decrease in yield [2]. Several crop-based indicators, including stomatal conductance, stem water potential, and canopy temperature, have been introduced to detect crop water status [5]. Stem water potential has been used to characterize crop water stress, showing a good correlation [9,10]. It has been shown to have a significantly negative relationship with the stomata; canopy temperature is a good indicator of water stress [14,15,16]. Canopy temperature alone does not immediately change with water status, which needs to be normalized to the environmental conditions [17]
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