Rice paddies are artificial wetlands that supply people with food and provide wildlife with habitats, breeding areas, shelters, feeding grounds and other services, and rice paddies play an important part in agricultural ecological systems. However, modern agricultural practices with large-scale intensive farming have significantly accelerated the homogenization of the paddy field ecosystem. Modern agriculture mostly relies on chemically-driven modern varieties and irrigation to ensure high production, resulting in the deterioration and imbalance of the ecosystem. Consequently, outbreaks of diseases, insects and weeds have become more frequent in paddy fields. This paper describes the current situation of rice paddy biodiversity in China and analyzes the community characteristics of arthropods and weedy plants. Meanwhile, we discuss how biodiversity was affected by modern agriculture changes, which have brought about a mounting crisis threatening to animals and plants once common in rice paddies. Measures should be focused to firstly preventing further deterioration and, then, also, promoting restoration processes. Ecological sustainability can be achieved by restoring paddy field biodiversity through protecting the ecological environment surrounding the paddy fields, improving paddy cropping patterns, growing rice with less agricultural chemicals and chemical fertilizers, constructing paddy systems with animals and plants and promoting ecological education and public awareness.

Saturated soil irrigation (SSI) has been reported as a great technique that increases water productivity in fully irrigated rice cultivation. However, this technique should be employed in a dry prone area where rainfalls fail to fulfill rice water requirements and fill up reservoirs for sufficient irrigation. Therefore, an experiment was conducted to identify the most effective complementary irrigation that restores soil moisture to saturation and increases water productivity with fewer yield expenses. The study used a complete randomized blocks design with four replications and four soil saturation treatments: Sat_200% (farmer practice), Sat_160%, Sat_120%, and Sat_80%. In Sat_160%, Sat_120%, and Sat_80% irrigation was applied once a week in the absence of rainfall. In Sat_200%, water was daily applied except the day of rain. The results showed that reductions in soil saturation decreased plant height, tillers, and leaves number by 10%, 18%, and 14%, respectively. Yield and components were reduced between 26.09% and 4.8%. Weekly soil saturation at 120% exhibited greater irrigation productivity (0.69 kg/m³), rainwater productivity (1.02 kg/m³), and water-saving (90.53%) with less production penalty (5 × 10⁻³ kg/m³). We advocate that saturated soil irrigation should be recommended in the rainfed rice system as a drought mitigation measure in semi-arid conditions.

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.