To investigate the effects of low-temperature (LT) stress on photosynthetic properties and senescence characteristics of winter wheat leaves during the jointing stage, an environmental temperature control experiment was designed at Nanjing University of Information Science and Technology in 2023, using Triticum aestivum L. cv. “Ji Mai 22” as the test material. Four different temperature levels were set: 18 °C/8 °C (daily maximum/daily minimum temperature; CK), 13 °C/3 °C, 10 °C/0 °C, and 7 °C/3 °C. The duration of each treatment was 2, 4, and 6 days, respectively. The experimental findings reveal that the changes in physiological parameters of winter wheat leaves under low-temperature stress treatments are nonlinear. Under the 3 °C LT treatment, the photosynthetic parameters and endogenous hormone levels of wheat leaves significantly decrease after 6 days of stress. Under the 0 °C LT treatment, the photosynthetic parameters, leaf pigment content, and endogenous hormones of wheat decrease significantly, while under the −3 °C LT treatment, all the parameters of winter wheat leaves show a significant decline. Generally, the “Ji Mai22” wheat cultivar has a lower growth temperature limit of −3 °C during the jointing stage.

With the expansion of cucumber cultivation, many growers continue to experience extreme weather and environmental issues. This study aimed to examine and model the effects of low temperature (LT) and poor light (PL) stresses on cucumber growth. The experiment was designed as an orthogonal experiment that analyzed temperature, light, and duration. The daily maximum/minimum temperatures of the experiment were set as per the following four levels: 13 °C/3 °C, 16 °C/6 °C, 19 °C/9 °C, 22 °C/12 °C, and the control at 28 °C/18 °C. The light was divided into two levels: 200 μmol∙m⁻²∙s⁻¹ and 400 μmol∙m⁻²∙s⁻¹, with 800 μmol∙m⁻²∙s⁻¹ as the control. Treatment duration was set at 2, 5, 8, and 11 days. Stress with different LT, PL, and duration was expressed using the stress effect (0–1), which decreased with an increase in stress level. Meanwhile, treatment with a temperature of 3 °C and light of 400 μmol∙m⁻²∙s⁻¹ for 11 days had the smallest effect on stress, which was only 67% of that of the control following 50 days of recovery, and had the most severe effect on cucumber growth. The proportion of dry weight allocated to leaves and stems decreased with increasing low temperatures and poor light stress, but the proportion allocated to fruit increased. The highest percentage of fruit distribution was found in the treatment with temperature of 9 °C, light of 200μmol∙m⁻²∙s⁻¹, and 11 days duration, being 3.57 times higher than the control. In order to better investigate the effects of LT and PL stress on cucumber growth, light and temperature effect (LTE), growing degree days (GDD), and product of thermal effectiveness and PAR (TEP) models were developed based on temperature and light. The root mean square error (RMSE) of the LTE model was found to be 4.214 g∙plant⁻¹, 36.3% of that of the GDD model and 78.8% of that of the TEP model, better simulating the above-ground dry weight of cucumber plants.

High temperatures have become a severe factor limiting growth and yield for tomatoes (Lycopersicon esculentum Mill.) due to unfavorable, above-optimum temperatures. Temperature and nitrogen application were the main regulatory factors in tomato plant cultivation. This research was undertaken to evaluate the effects of nitrogen application and high temperature on tomato morphology, dry matter accumulation and distribution, root vitality and nitrogen content of the above ground. Tomato variety “Jinfen No. 1” was planted and exposed to 4 day/night temperature levels (25 °C/15 °C as control CKT; 30 °C/20 °C, lightly high-temperature LHT; 35 °C/25 °C, moderate high-temperature MHT; 40 °C/30 °C, severe high temperature SHT) for 7 days after five nitrogen supply levels (N1–N5: 0, 1.3, 1.95, 2.6 and 3.75 g/plant, respectively; 2.6 g/plant is the recommended nitrogen-application rate, as control CKTN4). Within conditions, there was an extremely significant difference (p < 0.01) in all tomato plant parameters and an extremely significant interaction (p < 0.01) between high temperatures and nitrogen supply levels, except for plant height sampling on the 1st day in the recovery period. Dry matter accumulation decreased, and the accumulation rate slowed down. Dry matter accumulation under low nitrogen treatment was higher than in high nitrogen treatment. The proportion of dry matter in leaves decreased, the proportion of dry matter in stems increased and the difference in dry matter accumulation and proportion of dry matter between different nitrogen treatments decreased. Under LHT, the root activity of the tomato was increased under all nitrogen levels, while under MHT and SHT, high nitrogen and low nitrogen supply significantly inhibited root activity. Lightly high-temperature stress can increase root activity, and LHTN4 can increase by 5.15% compared with CKTN4. Appropriate nitrogen application can alleviate the damage caused by high-temperature stress on tomato plants and enhance the resistance of tomato plants, while excessive nitrogen application will aggravate the damage degree of tomato plants. In this study, the optimal nitrogen application rates under CKT-SHT treatment were 2.6, 2.6, 1.95 and 1.3 g/plant, respectively.

To study the impact of nitrogen application on the photosynthetic structure and photosystem activity of tomato (Lycopersicon esculentum Mill.) leaves during the recovery stage after heat stress, the OJIP curve and JIP parameters were determined through a control experiment in an artificial climate chamber. The tomato variety was “Jinfen No. 1”. Four day/night temperature levels (25 °C/15 °C as control CKT; 30 °C/20 °C, lightly high-temperature LHT; 35 °C/25 °C, moderate high-temperature MHT; 40 °C/30 °C, severe high-temperature SHT) were set for a duration of 7 days. Five nitrogen supply levels (N1–N5: 0, 1.3, 1.95, 2.6 and 3.75 g/plant, respectively; 2.6 g/plant is the recommended nitrogen application rate, CKTN4) were applied. The results showed that the O, K, J, I and P phases on the chlorophyll a fluorescence curve were significantly affected by different nitrogen treatments in heat stress recovery. Compared with CKT, with the increase in nitrogen supply, the fluorescence intensity of SHTN2-SHTN5 treatment increased significantly at P, I and J phases, while that of MHTN1-MHTN4 treatment decreased. The fluorescence intensity of SHTN5 and SHTN3 increased by 13.27% and 10.10% in the P phase, 13.52% and 12.1% in the I phase and 20.16% and 26.18% in the J phase, respectively. There were highly significant differences (p < 0.01) in the impact of high temperatures and nitrogen levels on the fluorescence parameters. On the 1st day after short-term heat stress, N had no significant effect on ${\mathrm{F}}_{\mathrm{v}}/{\mathrm{F}}_{\mathrm{M}}$, ${\mathrm{F}}_{\mathrm{v}}$, ${\mathrm{F}}_{\mathrm{o}}$ and ${\mathrm{F}}_{\mathrm{M}}$; however, their interaction was significant (p < 0.05). On the 8th day, there were no significant interaction effects between HT and N for ${\mathrm{F}}_{\mathrm{v}}/{\mathrm{F}}_{\mathrm{o}}$, ABS/RC and ${\mathrm{D}\mathrm{I}}_{\mathrm{o}}/\mathrm{R}\mathrm{C}$. ${\mathrm{F}}_{\mathrm{v}}/{\mathrm{F}}_{\mathrm{o}}$ proved to be sensitive to the application of both high temperatures and nitrogen. Under all five nitrogen applications, temperature played a significant role in increasing ${\mathrm{D}\mathrm{I}}_{\mathrm{o}}/\mathrm{R}\mathrm{C}$, especially for N2 and N3. The results indicated that decreasing the nitrogen application under SHT resulted in a higher number of active $\mathrm{R}\mathrm{C}\mathrm{s}$ and an increased value of specific energy flux ($\mathrm{A}\mathrm{B}\mathrm{S}/\mathrm{R}\mathrm{C}$, ${\mathrm{T}\mathrm{R}}_{\mathrm{o}}/\mathrm{R}\mathrm{C}$ and ${\mathrm{D}\mathrm{I}}_{\mathrm{o}}/\mathrm{R}\mathrm{C}$), indicating the enhanced ability of $\mathrm{R}\mathrm{C}$ to reduce plastoquinone. The study provides a reference for the diagnosis of nitrogen nutrition under high-temperature stress using chlorophyll fluorescence methods.

This study aimed to investigate the impact of low-temperature (LT) stress during the jointing stage on three most representative wheat varieties in the Huang-Huai-Hai region: “Shannong 38” (a robust winter wheat variety), “Jimai 22” (a semi-winter wheat variety), and “Zhenmai 12” (a weak winter wheat variety). The objective was to explain the sensitivity and change thresholds of various physiological and yield indicators of three winter wheat varieties to low temperatures during the jointing stage and to construct an index for the grading of LT disasters during the jointing stage using principal component analysis. Controlled environment experiments were conducted using an artificial climate chamber during the jointing stage of winter wheat. Five daily minimum temperature treatments were applied, namely (T1, −6 °C); (T2, −3 °C); (T3, 0 °C); (T4, 3 °C); (T5, 6 °C); and control (CK, 8 °C). The duration of treatments was divided into three levels: (D1: 2 days), (D2: 4 days), and (D3: 6 days). It was found that the photosynthetic parameters and chlorophyll content showed a decreasing trend with the increase in the degree of LT stress. The activities of protective enzymes and endogenous hormones increased during the early stages of LT stress or at relatively high temperatures. However, they decreased significantly with an increase in LT stress. Among the varieties, “Zhenmai 12” exhibited a yield reduction rate exceeding 10% under 3 °C LT stress for more than 4 days and a yield reduction rate exceeding 20% under 0 °C LT stress for more than 6 days. “Jimai 22” showed a yield reduction rate exceeding 10% under 3 °C LT stress lasting more than 2 days, and a yield reduction rate exceeding 20% under −3 °C LT stress lasting more than 4 days. “Shannong 38” experienced a yield reduction rate exceeding 10% under 0 °C LT stress lasting more than 4 days and a yield reduction rate exceeding 20% under −6 °C LT stress lasting more than 6 days. Principal component analysis (PCA) conducted on all trait indicators of the three winter wheat varieties revealed that “Zhenmai 12” experienced mild LT stress at 6 °C for 2 days, moderate LT stress at 0 °C for 6 days, and severe LT stress at −3 °C for 6 days. “Jimai 22” experienced mild LT stress under 6 °C for 6 days, moderate LT stress under 0 °C for 4 days, and severe LT stress under −6 °C for 2 days. “Shannong 38” experienced mild LT stress under 3 °C for 4 days, moderate LT stress under 0 °C for 4 days, and severe LT stress under −6 °C for 6 days.

Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges such as data accessibility and soil heterogeneous necessitate the use of numerical simulations for their effective regional-scale applications. The existing simulation methods like physical process models and machine learning (ML) algorithms have limitations: physical models struggle with parameter acquisition at regional scales, while ML algorithms face difficulties in agricultural settings due to the presence of crops. As a more advanced and complex branch of ML, deep learning algorithms face even greater limitations related to crop growth and agricultural management. To address these challenges, this study proposed a novel hybrid monitoring system that merged ML algorithms with a physical process model. Initially, we employed the Random Forest (RF) regression model that integrated multi-source environmental factors to estimate soil moisture prior to the sowing of winter wheat, achieving an average coefficient of determination (R²) of 0.8618, root mean square error (RMSE) of 0.0182 m³ m⁻³, and mean absolute error (MAE) of 0.0148 m³ m⁻³ across eight soil depths. The RF regression models provided vital parameters for the operation of the Water Balance model for Winter Wheat (WBWW) at a regional scale, enabling effective drought monitoring and assessments combined with the Soil Moisture Anomaly Percentage Index (SMAPI). Subsequent comparative analyses between the monitoring system-generated results and the actual disaster records during two regional-scale drought events highlighted its efficacy. Finally, we utilized this monitoring system to examine the spatiotemporal variations in drought patterns in the HHH region over the past two decades. The findings revealed an overall intensification of drought conditions in winter wheat, with a decline in average SMAPI at a rate of −0.021% per year. Concurrently, there has been a significant shift in drought patterns, characterized by an increase in both the frequency and extremity of drought events, while the duration and intensity of individual drought events have decreased in a majority of the HHH region. Additionally, we identified the northeastern, western, and southern areas of HHH as areas requiring concentrated attention and targeted intervention strategies. These efforts signify a notable application of multi-source data fusion techniques and the integration of physical process models within a big data context, thereby facilitating effective drought prevention, agricultural management, and mitigation strategies.