Crop evapotranspiration estimation is a key parameter for achieving functional irrigation systems. However, ET is difficult to directly measure, so an ideal solution was to develop a simulation model to obtain ET. There are many ways to calculate ET, most of which use models based on the Penman–Monteith equation, but they are often inaccurate when applied to greenhouse crop evapotranspiration. The use of machine learning models to predict ET has gradually increased, but research into their application for greenhouse crops is relatively rare. We used experimental data for three years (2019–2021) to model the effects on ET of eight meteorological factors (net solar radiation (R_n), mean temperature (T_a), minimum temperature (T_amin), maximum temperature (T_amax), relative humidity (RH), minimum relative humidity (RH_min), maximum relative humidity (RH_max), and wind speed (V)) using a greenhouse drip irrigated tomato crop ET prediction model (XGBR-ET) that was based on XGBoost regression (XGBR). The model was compared with seven other common regression models (linear regression (LR), support vector regression (SVR), K neighbors regression (KNR), random forest regression (RFR), AdaBoost regression (ABR), bagging regression (BR), and gradient boosting regression (GBR)). The results showed that R_n, T_a, and T_amax were positively correlated with ET, and that T_amin, RH, RH_min, RH_max, and V were negatively correlated with ET. R_n had the greatest correlation with ET (r = 0.89), and V had the least correlation with ET (r = 0.43). The eight models were ordered, in terms of prediction accuracy, XGBR-ET > GBR-ET > SVR-ET > ABR-ET > BR-ET > LR-ET > KNR-ET > RFR-ET. The statistical indicators mean square error (0.032), root mean square error (0.163), mean absolute error (0.132), mean absolute percentage error (4.47%), and coefficient of determination (0.981) of XGBR-ET showed that XGBR-ET modeled daily ET for greenhouse tomatoes well. The parameters of the XGBR-ET model were ablated to show that the order of importance of meteorological factors on XGBR-ET was R_n > RH > RH_min> T_amax> RH_max> T_amin> T_a> V. Selecting R_n, RH, RH_min, T_amax, and T_amin as model input variables using XGBR ensured the prediction accuracy of the model (mean square error 0.047). This study has value as a reference for the simplification of the calculation of evapotranspiration for drip irrigated greenhouse tomato crops using a novel application of machine learning as a basis for an effective irrigation program.

Ventilation and soil moisture influence greenhouse cultivation. Experiments were conducted at Xinxiang Irrigation Research Base of the Chinese Academy of Agricultural Sciences, Henan Province, China, to identify how ventilation and irrigation affected the greenhouse microenvironment. To develop ventilation and irrigation protocols that increase crop yield and improve the quality of drip-irrigated tomatoes grown in the greenhouse, three ventilation modes (T1, T2, and T3) were developed by opening vents in different locations in a completely randomized pattern. T1 had open vents on the north wall and roof of the greenhouse. T2 had open vents on the north and south walls and the roof. T3 had open vents on the north and south walls. Three irrigation treatments (W1, W2, and W3) were designed based on the accumulated water surface evaporation ( E _p ) of a standard 20-cm evaporation pan. The irrigation quantities were 0.9× E _p (W1), 0.7× E _p (W2), and 0.5× E _p (W3). The spatial and temporal distributions of temperature and humidity were analyzed for different combinations of ventilation and irrigation to identify their effects on tomato yield and fruit quality. Major results were as follows: 1) In addition to solar radiation, ventilation had an important influence on E _p and, on a daily scale, ventilation had a significant effect on E _p ( P < 0.05). 2) Ventilation had a significant effect on indoor wind speed, but the effect varied during different growth stages. During the flowering and fruit setting stage, wind speed for T2 significantly differed from those of T1 and T3 ( P < 0.01). During the harvest stage, the three ventilation treatments had significantly different effects ( P < 0.01). A correlation analysis showed high correlation between T2 wind speed and T3 wind speed ( R = 0.831), but low correlation between T2 wind speed and T1 wind speed ( R = 0.467). 3) The effect of ventilation on greenhouse humidity and temperature was greater than the effect of irrigation. The differences in air temperature among various combined treatments of ventilation and irrigation were significant for the flowering and fruiting stages ( P < 0.05), but they were not significant for the late harvest stage ( P > 0.05). There were significant differences in humidity on sunny days ( P < 0.01), but no significant differences on cloudy or rainy days ( P > 0.05). Air temperature at 2 m was greater than canopy temperature, but humidity at 2 m was less than that at canopy level. 4) Irrigation water quantity was positively correlated with tomato yield and negatively correlated with the fruit quality indicators total soluble solids, vitamin C content, organic acid content, and soluble sugars content. Ventilation had an effect primarily during the harvest period; it had no significant effect on yield ( P > 0.05). However, it had a significant effect on vitamin C content and the sugar:acid ratio ( P < 0.01). The combination treatment of T2W2 is recommended as the optimal treatment for greenhouse tomatoes using drip irrigation to produce an optimal combination of crop yield and fruit quality. This study provides theoretical and technical support for the improvement of greenhouse climate control by optimizing greenhouse ventilation and irrigation techniques to promote tomato yield and improve fruit quality.

Accurate short-term forecasts of daily reference evapotranspiration (ET₀) are essential for real-time irrigation scheduling. Many models rely on current and historical temperature data to estimate daily ET₀. However, easily accessible temperature forecasts are relatively less reported in short-term ET₀ forecasting. Furthermore, the accuracy of ET₀ forecasting from different models varies locally and also across regions. We used five temperature-dependent models to forecast daily ET₀ for a 7-day horizon in the North China Plain (NCP): the McCloud (MC), Hargreaves-Samani (HS), Blaney-Criddle (BC), Thornthwaite (TH), and reduced-set Penman–Monteith (RPM) models. Daily meteorological data collected between 1 January 2000 and 31 December 2014 at 17 weather stations in NCP to calibrate and validate the five ET₀ models against the ASCE Penman–Monteith (ASCE-PM). Forecast temperatures for up to 7 d ahead for 1 January 2015–19 June 2021 were input to the five calibrated models to forecast ET₀. The performance of the five models improved for forecasts at all stations after calibration. The calibrated RPM is the preferred choice for forecasting ET₀ in NCP. In descending order of preference, the remaining models were ranked as HS, TH, BC, and MC. Sensitivity analysis showed that a change in maximum temperature influenced the accuracy of ET₀ forecasting by the five models, especially RPM, HS, and TH, more than other variables. Meanwhile, the calibrated RPM and HS equations were better than the other models, and thus, these two equations were recommended for short-term ET₀ forecasting in NCP.