Analysis of the Spatial-Temporal Distribution Characteristics of Climate and Its Impact on Winter Wheat Production in Shanxi Province, China, 1964-2018.

The possible influence of global climate changes on agricultural production is becoming increasingly significant, necessitating greater attention to improving agricultural production in response to temperature rises and precipitation variability. As one of the main winter wheat-producing areas in China, the temporal and spatial distribution characteristics of precipitation, accumulated temperature, and actual yield and climatic yield of winter wheat during the growing period in Shanxi Province were analysed in detail. With the utilisation of daily meteorological data collected from 12 meteorological stations in Shanxi Province in 1964-2018, our study analysed the change in winter wheat yield with climate change using GIS combined with wavelet analysis. The results show the following: (1) Accumulated temperature and precipitation are the two most important limiting factors among the main physical factors that impact yield. Based on the analysis of the ArcGIS geographical detector, the correlation between the actual yield of winter wheat and the precipitation during the growth period was the highest, reaching 0.469, and the meteorological yield and accumulated temperature during this period also reached its peak value of 0.376. (2) The regions with more suitable precipitation and accumulated temperature during the growth period of winter wheat in the study area had relatively high actual winter wheat yields. Overall, the average actual yield of the entire region showed a significant increasing trend over time, with an upward trend of 47.827 kg ha-1 yr-1. (3) The variation coefficient of winter wheat climatic yield was relatively stable in 2008-2018. In particular, there were many years of continuous reduction in winter wheat yields prior to 2006. Thereafter, the impact of climate change on winter wheat yields became smaller. This study expands our understanding of the complex interactions between climate variables and crop yield but also provides practical recommendations for enhancing agricultural practices in this region.

Evaluation of Irrigation Modes for Greenhouse Drip Irrigation Tomatoes Based on AquaCrop and DSSAT Models.

The improvement of the simulation accuracy of crop models in different greenhouse environments would be better applied to the automation management of greenhouse cultivation. Tomatoes under drip irrigation in a greenhouse were taken as the research object, and the cumulative evaporation capacity (Ep) of the 20 cm standard evaporation dish was taken as the basis for irrigation. Three treatments were set up in the experiment: high water treatment without mulch (NM-0.9 Ep), high water treatment with mulch (M-0.9 Ep), and low water treatment with mulch (M-0.5 Ep). AquaCrop and DSSAT models were used to simulate the canopy coverage, soil water content, biomass, and yield of the tomatoes. Data from 2020 were used to correct the model, and simulation results from 2021 were analyzed in this paper. The results showed that: (1) Of the two crop models, the simulation accuracy of the greenhouse tomato canopy coverage kCC was higher, and the root mean square errors were less than 6.8% (AquaCrop model) and 8.5% (DSSAT model); (2) The AquaCrop model could accurately simulate soil water change under high water treatments, while the DSSAT model was more suitable for the conditions without mulch; (3) The relative error RE of simulated and observed values for biomass B, yield Y, and water use efficiency WUE in the AquaCrop model were less than 2.0%, 2.3%, and 9.0%, respectively, while those of the DSSAT model were less than 4.7%, 7.6%, and 10.4%, respectively; (4) Considering the simulation results of each index comprehensively, the AquaCrop model was superior to the DSSAT model; subsequently, the former was used to predict 16 different water and film coating treatments (S1-S16). It was found that the greenhouse tomato yield and WUE were the highest under S7 (0.8 Ep), at 8.201 t/ha and 2.79 kg/m3, respectively.

Root Distribution of Tomato Cultivated in Greenhouse under Different Ventilation and Water Conditions.

Mastering root distribution is essential for optimizing the root zone environment and for improving water use efficiency, especially for crops cultivated in greenhouses. Here, we set up two irrigation amount levels based on measurements of the cumulative 20 cm pan evaporation (Ep) (i.e., K0.9: 0.9 Ep; K0.5: 0.5 Ep), and three ventilation modes through opening the greenhouse vents at different locations (TR: open the roof vents only; TRS: open both the roof and south vents; TS: open the south vents only) to reveal the effects of the ventilation mode and irrigation amount on the root distribution of greenhouse tomato. Six treatments were designed in blocks with the ventilation mode as the main treatment and the irrigation amount as the vice treatment. On this basis, the normalized root length density (NRLD) model of six treatments was developed by considering air environment, soil water and temperature conditions, root length density (RLD) and yield. The results showed that air speed of the TRS was significantly higher than TR and TS (p < 0.01), and the air temperature and relative humidity under different ventilation showed the rule: TR > TS > TRS. There was a significant third-order polynomial function relationship between NRLD and soil depth, and the coefficient of the cubic term (R0) had a bivariate quadratic polynomial function relationship with irrigation amount and air speed (determination coefficient, R2 = 0.86). Root mean square errors of the simulated and measured value of NRLD under TR, TRS and TS were 0.20, 0.23 and 0.27 in 2020, and 0.31, 0.23 and 0.28 in 2021, respectively, normalized root mean squared errors were 15%, 17%, 20% in 2020, and 23%, 18% and 21% in 2021. The RLD distribution ratio from the ground surface to a one-quarter relative root depth was 74.1%, and 88.0% from the surface to a one-half relative root depth. The results of the yield showed that a better combination of ventilation and irrigation was recommended as TRS combined with K0.9.

Performance of the Improved Priestley-Taylor Model for Simulating Evapotranspiration of Greenhouse Tomato at Different Growth Stages.

Mastering crop evapotranspiration (ET) and improving the accuracy of ET simulation is critical for optimizing the irrigation schedule and saving water resources, particularly for crops cultivated in a greenhouse. Taking greenhouse-grown tomato under drip irrigation as an example, two weighing lysimeters were used to monitor ET at two seasons (2019 and 2020), whilst meteorological factors inside the greenhouse were measured using an automatic weather station. Then the path analysis approach was employed to determine the main environmental control factors of ET. On this basis, an improved Priestley-Taylor (IPT) model was developed to simulate tomato ET at different growth stages by considering the influence of environmental changes on model parameters (e.g., leaf senescence coefficient, temperature constraint coefficient and soil evaporative water stress coefficient). Results showed that the average daily ET varied from 0.06 to 6.57 mm d−1, which were ~0.98, ~2.58, ~3.70 and ~3.32 mm/d at the initial, development, middle and late stages, respectively, with the total ET over the whole growth stage of ~333.0 mm. Net solar radiation (Rn) and vapor pressure deficit (VPD) were the direct influencing factors of ET, whereas air temperature (Ta) was the limiting factor and wind speed (u2) had a little influence on ET. The order of correlation coefficients between meteorological factors and ET at two seasons was Rn > VPD > Ta > u2. The IPT model can accurately simulate ET in hourly and daily scales. The root mean square error of hourly ET at four stages changed from 0.002 to 0.08 mm h−1 and daily ET varied from 0.54 to 0.57 mm d−1. The IPT coefficient was close to the recommended PT coefficient (1.26) when the average Ta approaches 26 °C and LAI approaches 2.5 cm2 cm−2 in greenhouse conditions. Our results can provide a theoretical basis for further optimization of greenhouse crop irrigation schedules and improvement of water use efficiency.

Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model.

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 (Rn), mean temperature (Ta), minimum temperature (Tamin), maximum temperature (Tamax), relative humidity (RH), minimum relative humidity (RHmin), maximum relative humidity (RHmax), 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 Rn, Ta, and Tamax were positively correlated with ET, and that Tamin, RH, RHmin, RHmax, and V were negatively correlated with ET. Rn 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 Rn > RH > RHmin> Tamax> RHmax> Tamin> Ta> V. Selecting Rn, RH, RHmin, Tamax, and Tamin 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.