Plastic film mulching has been widely used to improve crop yield and water use efficiency, although the effects of plastic film mulching on water, heat, nitrogen dynamics, and crop growth are rarely presented comprehensively. This study investigated a large number of studies in film mulching fields from the past 10 years (mostly from 2019 to 2023) and summarized the impact of plastic film mulching, progress in modeling with film mulching, and future research directions. The effects of plastic film mulching were intricate and were influenced by film mulching methods, irrigation systems, crop types, crop growth stages, etc. Overall, plastic film mulching showed a positive effect on improving soil water, temperature, and nitrogen status, enhancing crop transpiration and photosynthetic rates, and promoting crop growth and yield, although film mulching may have negative effects, such as increasing rainfall interception, blocking water entering the soil, and reducing net radiation income. The crop yield and water use efficiency could increase by 39.9–84.7% and 45.3–106.4% under various film mulching methods. Coupled models of soil water and heat transport and crop growth under plastic film mulching conditions have been established by considering the effects of plastic film mulching on the upper boundary conditions of soil water and heat, energy budget and distribution processes, and the exchange of latent and sensible heat between soil and atmosphere. The models have good applicability in film mulched farmland of maize, rice, and potato for different regions of China. Further development is needed for soil water, heat, nitrogen migration, and crop growth models under different plastic film mulching methods, and the acquisition of soil and crop indicators under plastic film mulching conditions based on big data support. The study will provide reference for the subsequent development and innovation of plastic film mulching technology.

Agriculture is the most important sector that is consuming water resources. In the context of global water scarcity, how to use limited water resources to improve water use efficiency in agriculture or achieve maximum crop yield and fruit quality is of great significance for ensuring food and water security. Optimizing irrigation schedules is an effective measure to improve water use efficiency, where crop models also play an important role. However, there is little research summarizing the optimization of irrigation schedules based on crop models. This study provides a systematic review on how to optimize irrigation schedules based on crop models and simulation–optimization models. When optimizing irrigation schedules based on crop models, the selected models are usually mechanistic agro-hydrological models. Irrigation scenarios and optimization objectives are mainly focused on both crop and water aspects, such as maximizing crop yield, fruit quality, water productivity, and irrigation water productivity. Minimizing crop water consumption and total irrigation amounts serve as optimization objectives, and irrigation quantity, irrigation frequency, and irrigation interval serve as decision variables. In saline areas or low fertilizer utilization areas, the optimization objectives and decision variables also involve some indicators related to salt and nitrogen, such as the maximum desalination rate, minimum salt content, fertilizer utilization efficiency, nitrogen fertilizer productivity, nitrogen fertilizer utilization efficiency, nitrogen leaching rate, which serve as the optimization objectives, and the irrigation water salinity, or fertilization schedules serve as the decision variables. When optimizing irrigation schedules based on simulation–optimization models, the models have mainly been upgraded from water-production function to crop mechanism models. In addition, optimization algorithms have been upgraded from traditional optimization techniques to intelligent optimization algorithms. Decision-making techniques are used to make decisions on optimization results. In addition, the spatial scale for the optimization problem of irrigation schedules was developed from fields to regions, and the time scale was developed from the growth stage, beginning with months, and shortening to ten days, then to a day, and then to an hour. This study also provides a detailed introduction to widely used optimization algorithms, such as genetic algorithms, as well as decision techniques. At the same time, it is proposed that the future should focus on improving crop models and analyzing uncertainty in research on irrigation schedule optimization, which is of great significance for the precise regulation of irrigation schedules.

The Soil–Water–Atmosphere–Plant (SWAP) model does not have a mulching module to simulate the effect of film mulching on soil water, heat dynamics and crop growth. In this study, SWAP model parameters were selected to simulate the soil water–heat process and crop growth, taking into account the effect of film mulching on soil evaporation, temperature, and crop growth, in order to predict the influence of future climate change on crop growth and evapotranspiration (ET). A most suitable scheme for high yield and water use efficiency (WUE) was studied by an experiment conducted in the Shiyang River Basin of Northwest China during 2017 and 2018. The experiment included mulching (M1) and non-mulching (M0) under three drip irrigation treatments, including full (WF), medium (WM), low (WL) water irrigation. Results demonstrated that SWAP simulated soil water storage (SWS) well, soil temperature at various depths, leaf area index (LAI) and aboveground dry biomass (ADB) with the normalized root mean square error (NRMSE) of 16.2%, 7.5%, 16.1% and 16.4%, respectively; and yield, ET, and WUE with the mean relative error (MRE) of 10.5%, 12.4% and 14.8%, respectively, under different treatments on average. The measured and simulated results showed film mulching could increase soil temperature, promote LAI during the early growth period, and ultimately improve ADB, yield and WUE. Among the treatments, M1WM treatment with moderate water deficit and film mulching could achieve the target of more WUE, higher yield, less irrigation water. Changes in atmospheric temperature, precipitation, and CO₂ concentration are of worldwide concern. Three Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP8.5) showed a negative effect on LAI, ADB and yield of seed-maize. The yield of seed-maize on an average decreased by 33.2%, 13.9% under the three RCPs scenarios for film mulching and non-mulching, respectively. Predicted yields under film mulching were lower than that under non-mulching for the next 30 years demonstrating that current film mulching management might not be suitable for this area to improve crop production under the future climate scenarios.

Global climate change has posed a serious threat to agricultural production and food security. Crop growth models, with their excellent simulation and prediction capabilities, have become one of the important tools for guiding agricultural production and ensuring food security. This study provides a review of the research progress in crop growth models. The development history of the models is summarized into four stages, including process modeling, system simulation, model application, and algorithm expansion. According to different driving factors, the crop growth models can be categorized into solar radiation-driven, soil moisture content-driven, meteorological factor-driven, and integrated factor-driven models. In terms of the countries of development, the models mainly include those from the Netherlands, the United States, Australia, and China. Regarding applications, crop growth models are primarily applicable to adaptability assessment, agricultural resource and crop cultivation management, and climate change evaluation. Since their initial development, these models have enhanced their mechanistic nature through various approaches, such as incorporating surface mulching modules, considering the response of root water uptake to soil salt stress, and preliminarily introducing physiological regulation processes. By integrating with remote sensing technology, the spatial scale of the models has been expanded from the point scale to the regional or even global scale, and the accuracy of regional-scale yield estimation has been significantly improved through assimilation with remote sensing. Meanwhile, crop growth models have also been combined with intelligent algorithms to optimize irrigation scheduling, and to perform model parameter optimization. Looking forward, potential future research directions of crop growth models include extending the soil submodule from one-dimensional to two/three-dimensional water–heat–solute transport, moving toward a more mechanistic crop growth modeling, integration with remote sensing, and incorporating artificial intelligence. This study serves as a reference for further development and application of crop growth models, and provides technical support for the development of sustainable agriculture.