Wei Zeng

How to precisely depict the effects of high temperature on greenhouse crop growth and yield formation and derive optimal irrigation schedules remains an emerging challenge for current crop models. To address this issue, our study focused on drip-irrigated tomato plants grown in a greenhouse and adopted a two-factor experimental design with high temperature (TH: daily maximum temperature 35~40 °C) and two water levels (full irrigation, WH: 1.0E_pan; deficit irrigation, WD: 0.6E_pan), where E_pan denotes the cumulative evaporation of a 20 cm standard evaporation pan. Forty-three parameters of the AquaCrop-OSPy model were screened using the Morris global sensitivity analysis method, and the selected highly sensitive parameters were processed via GLUE-based calibration and uncertainty characterization subjected to global optimization through a GLUE method to derive the model-based preferred strategy under high-temperature conditions in the greenhouse. On this basis, four indicators (yield, biomass (Bio), water consumption (ET) and water use efficiency (WUE)) were adopted to evaluate 20 combined scenarios of five temperature gradients and four irrigation strategies, which were then ranked using the TOPSIS–Entropy Weight (TEW) method. The results showed that the highly sensitive parameters of the AquaCrop-OSPy model under high-temperature greenhouse conditions included initial harvest index, water productivity, basal crop coefficient, maximum canopy cover, canopy growth coefficient, and days to maturity. After optimization using the GLUE method, the values of the above parameters were 70.9%, 15.2 g/m³, 1.36, 86.2%, 12.6%/d, and 108 d, respectively. The calibrated AquaCrop-OSPy model performed satisfactorily for canopy cover (CC), Bio, and yield under the evaluated experimental conditions, although ET and WUE simulations remained less accurate under combined high-temperature and deficit-irrigation conditions. The root mean square error of simulated CC was less than 7.2%, and the percentage errors of yield and Bio were less than 3.5% and 6.9%, respectively. Evaluation of the 20 temperature–irrigation coupled scenarios revealed that, among the scenarios evaluated, the 32~34 °C range combined with 1.1 or 0.9 E_pan was associated with comparatively high simulated yield and WUE. Among the simulated scenarios evaluated using the TEW procedure, 34 °C + 1.1 E_pan produced the highest comprehensive index (0.9283). The framework provides a model-based basis for identifying candidate irrigation strategies for greenhouse tomato under high-temperature conditions, which should be further tested experimentally.

High temperature is a primary meteorological hazard restricting crop yield and quality, particularly in semi-automatic greenhouses. However, the accuracy of crop models under controlled high-temperature conditions in greenhouses has rarely been examined. To address this, our study evaluated the AquaCrop (7.1) and DSSAT (4.8.2) models for greenhouse-grown tomato under high-temperature conditions. Field experiments were conducted over two consecutive years (2024 and 2025) with two temperature treatments (TH: daily maximum temperature > 35 °C; TD: daily maximum temperature < 35 °C) and two irrigation levels (WH: 100%E_pan; WD: 60%E_pan, where E_pan is cumulative pan evaporation). Key indicators were continuously measured, including canopy cover, soil water content, above-ground biomass, yield, and water consumption. Additionally, the XGBoost algorithm was introduced to estimate reference crop evapotranspiration across different treatment combinations, facilitating a multi-scenario analysis. The results indicated that under both TD and TH conditions, the AquaCrop model accurately simulated canopy cover (RMSE ≤ 6.84%, NRMSE ≤ 12.27%, EF ≥ 0.92), yield (PE ≤ 12.20%), above-ground biomass (PE ≤ 2.34%), and water consumption (PE ≤ 10.33%) across different irrigation levels. However, its performance in simulating soil water content declined markedly under the TH treatment, with EF becoming negative in some cases (−1.66 ≤ EF ≤ 0.43), indicating that the model was largely ineffective for SWC under these conditions. The DSSAT model exhibited lower simulation accuracy under the TH treatment compared to the TD treatment, and its overall performance was slightly inferior to that of the AquaCrop model (RMSE ≤ 15.79 mm, NRMSE ≤ 14.54%, −1.68 ≤ EF ≤ 0.46). Scenario analysis of 20 temperature and irrigation combinations, integrating XGBoost-derived ET₀ into the AquaCrop model, revealed that maximizing greenhouse tomato yield requires a combination strategy of 1.1E_pan and 34 °C, whereas optimizing water use efficiency is best achieved under 0.9E_pan and 32 °C. These findings provide a theoretical basis and technical support for the application of crop models and the development of environmental regulation strategies in greenhouse cultivation under high-temperature conditions.

Calibration of crop model parameters using optimization algorithms can substantially improve model performance, particularly under different environmental scenarios. Here, we take greenhouse drip-irrigated tomato as an example. Two temperature treatments (high temperature, TH: 35 °C ≤ T_max ≤ 40 °C; non-high temperature, TD: T_max ≤ 35 °C) and two water treatments (well watered, WH: 100%E_pan; water deficit, WD: 60%E_pan, where E_pan is the pan evaporation coefficient) were combined and implemented in 2024 and 2025. Canopy cover, yield, aboveground biomass, and water consumption of tomatoes were measured, and then a global sensitivity analysis was conducted using the extended Fourier amplitude sensitivity test (EFAST). Thereafter, in order to evaluate the performance of AquaCrop-OSPy under different combinations of temperature and water conditions, the whale optimization algorithm (WOA) was coupled with the AquaCrop-OSPy model, and an automatic parameter optimization framework was developed using the aforementioned measured indicators as objective functions. The results showed that CC_x, Senescence_CD, CGC_CD, WP, HI₀, and K_cb were highly sensitive parameters of the AquaCrop-OSPy model across different temperature and water treatments. Compared with the traditional trial and error (TAE) method, WOA demonstrated superior global optimization capability and significantly improved model performance. Validation indicated that the root mean square error (RMSE) was ≤4.52% for canopy cover, ≤0.67 t/hm² for yield, and ≤0.98 t/hm² for aboveground biomass. Notably, even after WOA optimization, water consumption simulation under combined high temperature and water deficit conditions still showed some deviation, which was attributed to limitations in soil water simulation and the oversimplification of model mechanisms under combined stress. Nevertheless, the model remained reliable for estimating canopy cover, yield, and biomass, while ET simulation under combined stress should be interpreted with caution. Therefore, future improvements of AquaCrop-OSPy should focus on addressing the effects of soil water and refining the inhibitory feedback of physiological stress to better adapt the model to the combined conditions of high temperature and water deficit.

The temporal variations of evapotranspiration (ET) and its controlling factors occur across time scales ranging from seconds to decades, with significant differences in the lag effects of ET drivers under varying water conditions. Therefore, identifying the dominant time scales of the relationships between ET and its controlling factors under varying water conditions is crucial for optimizing irrigation strategies of crops grown in a greenhouse. In our study, we utilized two years of continuous lysimeter observations of greenhouse tomato ET, and applied two water treatments: well-irrigated (0.9E_pan, E_pan is the cumulative pan evaporation) and deficit-irrigated (0.5E_pan). Wavelet transform technology served as the core method to systematically examine the temporal variations of ET and its controlling factors. Observations indicated that the power spectra of ET featured pronounced peaks at daily and seasonal scales. The cospectra between ET and soil water content for greenhouse tomato revealed strong temporal correlation at 2~5 day scales, confirming the regulatory effect of irrigation cycles on ET. Moreover, ET variations were largely synchronous with net radiation, with ET lagging net radiation but leading vapor pressure deficit and air temperature at daily scales. In addition, significant disparities in phase angles between ET and individual meteorological variables were identified under 0.9E_pan and 0.5E_pan water conditions. Partial wavelet coherence revealed that net radiation was the primary meteorological driver of greenhouse tomato ET across multiple time scales, particularly at the daily scale, followed by vapor pressure deficit. These findings provide scientific evidence for selecting appropriate ET models at different time scales and offer valuable insights for optimizing water-saving irrigation for crops grown in greenhouses.

This study presents a comprehensive bibliometric analysis of the DSSAT crop modeling field from 1990 to 2024, identifying its evolutionary trajectory and emerging frontiers. A comprehensive bibliometric analysis and network visualization were conducted using VOSviewer, CiteSpace, and Bibliometrix. Analyzing 6165 Scopus-indexed publications, we found the research focus has shifted from foundational yield simulation and calibration toward addressing complex climate-water-food challenges. Three distinct developmental phases were identified: an initial establishment phase, a methodological refinement phase, and a current technology integration phase dominated by machine learning and remote sensing applications. The results reveal that machine learning, model-data fusion, and sustainability assessment represent the most active research frontiers. This analysis provides a systematic map of the field’s intellectual structure and offers evidence-based predictions for its future development, highlighting the transition of DSSAT from a specialized crop model to an interdisciplinary decision-support platform for sustainable agriculture.