In the context of the rapid transformation of global agricultural production towards intensification and intelligence, the precise and intelligent management of horticultural crop production processes is key to enhancing resource utilization efficiency and industry profitability. Crop growth and development models, as digital representations of the interactions between environment, crops, and management, are core tools for achieving intelligent decision-making in facility production. This paper provides a comprehensive review of the advancements in intelligent management models and systems for horticultural crop growth and development. It introduces the developmental stages of horticultural crop growth models and the integration of multi-source data, systematically organizing and analyzing the modeling mechanisms of crop growth and development process models centered on developmental stages, photosynthesis and respiration, dry matter accumulation and allocation, and yield and quality formation. Furthermore, it summarizes the current status of expert decision-support system software development and application based on crop models, achieving comprehensive functionalities such as data and document management, model parameter management and optimization, growth process and environmental simulation, management plan design and effect evaluation, and result visualization and decision product dissemination. This illustrates the pathway from theoretical research to practical application of models. Addressing the current challenges related to the universality of mechanisms, multi-source data assimilation, and intelligent decision-making, the paper looks forward to future research directions, aiming to provide theoretical references and technological insights for the future development and system integration of intelligent management models for horticultural crop growth and development.

Nitrate leaching, greenhouse gas emissions, and water loss are caused by conventional water and fertilizer management in vegetable fields. The Expert-N system is a useful tool for recommending the optimal nitrogen (N) fertilizer for vegetable cultivation. To clarify the fates of water and N in vegetable fields, an open-field vegetable cultivation experiment was conducted in Dongbeiwang, Beijing. This experiment tested two irrigation treatments (W1: conventional and W2: optimal) and three fertilizer treatments (N1: conventional, N2: optimal N rate by Expert-N system, and N3: 80% optimal N rate) on cauliflower (Brassica oleracea L.), amaranth (Amaranthus tricolor L.), and spinach (Spinacia oleracea L.). The EU-Rotate_N model was used to simulate the fates of water and N in the soil. The results indicated that the yields of amaranth and spinach showed no significant differences among all the treatments in 2000 and 2001. However, cauliflower yield under the W1N2 and W1N3 treatments obviously reduced in 2001. Compared with the W1 treatment, W2 reduced irrigation amount by 27.9–29.8%, water drainage by over 76%, increased water use efficiency by 5–17%, and irrigation water use efficiency by 29–45%. Nitrate leaching was one of the main pathways in this study, accounting for 8.4% of the total N input; compared to N1, the input of fertilizer N under the N2 and N3 treatments decreased by over 66.5%, consequently reducing gaseous N by 48–72% and increasing nitrogen use efficiency (NUE) by 17–37%. Additionally, compared with the W1 treatments, gaseous N loss under the W2 treatments was reduced by 18–26% and annual average NUEs increased by 22–29%. The highest annual average NUEs were under W2N3 (169.6 kg kg⁻¹) in 2000 and W2N2 (188.0 kg kg⁻¹) in 2001, respectively. We found that optimizing fertilizer management allowed subsequent crops to utilize residual N in the soil. Therefore, we suggest that the W2N3 management should be recommended to farmers to reduce water and N loss in vegetable production systems.

The North China Plain, a major grain production base in China, is facing the chronic threat of climate-change-induced delays in winter wheat sowing, with late sowing constraining yields by shortening the pre-winter growth period, and soil moisture at sowing potentially serving as a key factor to alleviate late-sowing losses. However, previous studies have mostly independently analyzed the effects of sowing time or water stress, and there is still a lack of systematic quantitative evaluation on how the interaction effects between the two determine long-term yield potential and risk. To fill this gap, this study aims to quantify, in the context of long-term climate change, the independent and interactive effects of different sowing dates and baseline soil moisture on the growth, yield, and production risk of winter wheat in the North China Plain, and to propose regionally adaptive management strategies. We selected three representative stations—Beijing (BJ), Wuqiao (WQ), and Zhengzhou (ZZ)—and, using long-term meteorological data (1981–2025) and field trial data, undertook local calibration and validation of the APSIM-Wheat model. Based on the validated model, we simulated 20 management scenarios comprising four sowing dates and five baseline soil moisture levels to examine the responses of phenology, aboveground dry matter, and yield, and further defined yield-reduction risk probability and expected yield loss indicators to assess long-term production risk. The results show that the APSIM-Wheat model can reliably simulate the winter wheat growing period (RMSE 4.6 days), yield (RMSE 727.1 kg ha⁻¹), and soil moisture dynamics for the North China Plain. Long-term trend analysis indicates that cumulative rainfall and the number of rainy days within the conventional sowing window have risen at all three sites. Delayed sowing leads to substantial yield reductions; specifically, compared with S1, the S4 treatment yields about 6.9%, 16.2%, and 16.0% less at BJ, WQ, and ZZ, respectively. Moreover, increasing the baseline soil moisture can effectively compensate for the losses caused by late sowing, although the effect is regionally heterogeneous. In BJ and WQ, raising the baseline moisture to a high level (P85) continues to promote biomass accumulation, whereas in ZZ this promotion diminishes as growth progresses. The risk assessment indicates that increasing baseline moisture can notably reduce the probability of yield loss; for example, in BJ under S4, elevating the baseline moisture from P45 to P85 can reduce risk from 93.2% to 0%. However, in ZZ, even the optimal management (S1P85) still carries a 22.7% risk of yield reduction, and under late sowing (S4P85) the risk reaches 68.2%, suggesting that moisture management alone cannot fully overcome late-sowing constraints in this region. Optimizing baseline soil moisture management is an effective adaptive strategy to mitigate late-sowing losses in winter wheat across the North China Plain, but the optimal approach must be region-specific: for BJ and WQ, irrigation should raise baseline moisture to high levels (P75-P85); for ZZ, the key lies in ensuring baseline moisture crosses a critical threshold (P65) and should be coupled with cultivar selection and fertilizer management to stabilize yields. The study thus provides a scientific basis for regionally differentiated adaptation of winter wheat in the North China Plain to address climate change and achieve stable production gains.