Winter extreme low temperature events have been occurring frequently both before and after the winter season. The freezing resistance temperature of wheat is far lower than the intensity of low temperatures during the mid-winter period. Therefore, it is necessary to further quantify and evaluate the impact of low-temperature periods and durations during the early winter and the green-up period on the freezing resistance of wheat, based on different evaluation indicators. Through conducting experiments in an artificial low-temperature control chamber, this study investigates the critical temperature thresholds for the impact of different low-temperature periods and durations on the tiller and yield of winter wheat, as well as the critical temperature thresholds for soil effective negative accumulated temperature. The results demonstrate that (1) the tiller mortality rate (R_T) and yield reduction rate (R_Y) of winter wheat during the winter increase with the severity and duration of low temperatures, showing an S-shaped curve. The winter wheat mortality rate during the early winter is related to the soil effective negative accumulated temperature in an exponential function, while the mid-winter and green-up stages have a linear relationship. (2) The freezing threshold temperatures for the R_T, R_Y and soil negative accumulated temperature (SENAT) in different low-temperature periods (early winter, mid-winter, and green-up periods) range from − 11.7 to -17.9 °C, -9.4 to -15.6 °C, and 15.9 to 131.7 °C·h (2.2 to 16.8 °C·d), respectively. (3) The freezing threshold temperatures for the R_T and R_Y in different low-temperature durations (1 day, 2 days, and 3 days) range from − 2.8 to -17.9 °C and − 9.4 to -15.6 °C, respectively. The findings of this study provide technical support and scientific guidance for the global cultivation structure and variety layout of winter wheat under the background of climate warming, as well as for the prevention and reduction of freezing damage and yield losses.

Winter wheat (Triticum aestivum L.) is a crucial crop that guarantees food supply in the North China Plain (NCP). As the frequency of extreme cold events increases, it is necessary to explore the freezing resistance of different wheat varieties in order to clarify planting boundaries and help with risk assessment. In this study, 2-year controlled experiments were conducted to explore the effect of freezing temperatures (T_air) and freezing durations on three winterness types. A set of indexes were used to characterize the subfreezing stress on wheat tiller, leaf, and final yield. Logistical regressions were used to quantify the temperature threshold for 10%, 30%, and 50% of freezing injury. The results showed that the lower temperature threshold of tiller (LT) varied from −9.6 to −15.9°C, −10.7 to −19.1°C and −11.4 to −21.2°C for LT₁₀, LT₃₀, and LT₅₀, respectively. The difference between LT and yield loss (YL) indexes reduced with decreased winterness types and was −0.1 to 3.4°C, −0.7 to 2.1°C, and 0.3 to 0.9°C higher compared with YL thresholds for winterness, semi-winterness, and weak-winterness types, respectively. The average minimum soil temperature was 7.5, 4.8, and 4.2°C higher than T_air for 1-, 2-, and 3-day treatment, respectively. Soil effective negative accumulated temperature hours (TSE_h) ranged from 6.9 to 12.0, 48.4 to 6.9, and 84.7 to 106.9°C·h for 10%, 30%, and 50% tiller mortality, respectively. Freezing treatment with T_air < −12, −9, and −8°C obviously decreased leaf Fv/Fm for the three varieties and Fv/Fm declined obviously after 5 days of recovery under field conditions. Our results provided multiple indexes for quantifying subfreezing damage in practical wheat production and could shed light on future risk assessment.

The Cucumber Productivity Simulation Model (CPSM) was developed to precisely predict the dynamic process of cucumber productivity in a solar greenhouse. This research conducted a variety of sowing experiments and collected data on cucumber productivity and meteorological conditions from 2013 to 2015 and 2018 to 2020. Employing the principles of least squares, the relationship between cucumber productivity indicators and effective accumulated temperature (EAT) was fitted, determining key crop parameters and constructing the CPSM. Validation of the model was conducted using independent experimental data, evaluating its simulation accuracy. The results indicate that (1) CPSM can dynamically and meticulously simulate the formation process of different productivity indicators in cucumber. Normalized root mean square errors (NRMSE) ranged from 0.44% to 19.64%, and mean relative errors (MRE) ranged from 0.31% to 17.23% across different productivity indicator models. The models for organ water content, maximum root length, specific leaf area, and organ fresh weight distribution index demonstrated high simulation accuracy, while others showed relatively high accuracy. (2) Simulation accuracy varied with indicators and varieties. 19 indicators (34.55%) exhibited high simulation accuracy and 30 indicators (54.55%) showed relatively high accuracy. The JY35 variety (10.44 ± 8.49%) outperformed the JS206 variety (13.44 ± 8.50%) in terms of simulation accuracy. The JY35 variety had 39 superior productivity indicators (70.91%) while the JS206 variety had sixteen (29.09%). CPSM utilizes easily accessible temperature data as its input, allowing for precise and detailed simulation of productivity indicators for cucumber production in solar greenhouses. This research lays a theoretical foundation and provides technical support for guiding intelligent production management, efficient utilization of agricultural resources, and climate change productivity assessment in solar greenhouse cucumber production.

Great concern has long been raised about nitrate leaching in cropland due to its possible environmental side effects in ground water contamination. Here we employed two common techniques to measure nitrate leaching in tea plantation soils in subtropical China. Using drainage lysimeter as a reference method, the adaptability of estimating drainage and nitrate leaching by combining the water balance equation with the suction cup technique was investigated. Results showed that the final cumulative leachate volume for the calculated and measured method was 721.43 mm and 729.92 mm respectively during the study period. However, nitrate concentration exerted great influence in the estimation of nitrate leaching from the suction cup-based method. The cumulative nitrate leaching loss from the lysimeter and suction cup-based method was 47.45 kg ha⁻¹ and 43.58 kg ha⁻¹ under lysimeter nitrate concentrations ranging from 7 mg L⁻¹ to 13 mg L⁻¹, 156.28 kg ha⁻¹ and 79.95 kg ha⁻¹ under lysimeter nitrate concentrations exceeding 13 mg L⁻¹. Therefore, the suction cup-based method could be an alternative way of monitoring nitrate leaching loss within a range of 7–13 mg L⁻¹ of nitrate concentrations in leachate. Besides, lower results occurred in suction cup samplers due to lack of representative samples which mainly leached via preferential flow when in strong leaching events. Thus, it is advisable to increase sampling frequency under such special conditions. The results of this experiment can serve as a reference and guidance for the application of ceramic cups in monitoring nitrogen and other nutrient-ion leaching in tea plantation soils.

The functioning of ecosystem services in water-limited grassland ecosystems is significantly influenced by precipitation characteristics. This study aims to quantitatively assess the impact of different precipitation scenarios on grassland productivity using the APSIM model. Historical weather data from 1968 to 2017 and observational data from three types of steppes (meadow, typical, and desert steppe) in Inner Mongolia Autonomous Region from 2004 to 2010 were collected to determine key crop variety parameters for the APSIM model. The effects of annual precipitation, seasonal precipitation, and inter-growing season precipitation variability on aboveground net primary production (ANPP) and precipitation utilization efficiency (PUE) in different types of steppes were investigated by scenario simulation by validated model. The simulated ANPP shows distinctive responses to the changed rainfall characteristics, where the influence of precipitation decreasing is more evident than precipitation increasing by the same precipitation change. Regarding steppe types, the typical steppe responded more strongly to increased precipitation, while decreased precipitation led to higher decline in ANPP for desert steppe. Precipitation during growing seasons caused more significant change than dormancy seasons regarding ANPP, however, PUE show the opposite trend, indicating the contribution of unit level precipitation changes to productivity is significant during dormancy seasons. The effect of changing precipitation during middle growing season outweighed that of late growing season and early growing season, and the positive effect of increasing precipitation were more pronounced in typical steppe and desert steppe if facing early growing season precipitation increase in the future. The research results provide a theoretical basis and technical support for optimizing grassland production management.

Crop model is designed to dynamically and accurately simulate the growth process of crops, and according to people's demand for tulip, it is urgent to establish a simpler and more accurate growth model. In this study, tulip observation data was collected from experiments with six popular cultivars of tulip ('Pink Impression', 'Huge Pink', Talibelac 'Daydream', 'Esmee' and 'Queen of Night'), which respectively represent different florescence of Early blossoming (EB), Middle blossoming (MB), Late blossoming (LB) and Very late blossoming (VB). Multi-sowing period treatment experiments were carried out in two growing seasons. The growth model for main organs morphology of tulip in this study is constructed by a new method named standardized growing degree days (Stan,). The results showed that 1) Flowering stage is not affected by variety, but withering stage is affected; 2) In the whole growth process of tulip flowers, there were significant relationships between the growth of different florescence tulips and different organs (root, bulb, stem, leaf and flower) and (Stan D), including linear, quadratic, logistic curve, power, and logarithmic functions; 3) NRMSE of the length index class, area class, proportion class, weight class and SLA simulation model respectively were within the range of 3.40%-29.22%, 17.34%-26.44%, 19.92%-23.85%, 8.26% 24.66% and 9.71%, and RMSE respectively were within the range of 0.25-8.19 cm, 3.36-47.80 cm2, 0.29-1.86, 0.26-9.58 g plant' and 1.27 m2 kg'. In general, the growth model of different organs had high simulation accuracy. This study will be helpful in quantifying the morphology of each organ and flowering stage in solar greenhouse by calculating (StanGDD).