A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01–0.41 kg m⁻²), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m⁻² for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m⁻². Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs.

Adjusting the sowing dates of crops is an effective measure for adapting them to climate change, but very few studies have explained how the optimum sowing dates can be determined. In this study, we used the sowing date field data from 2018 to 2021 from Hebei Gucheng Agricultural Meteorology National Observation and Research Station to analyze the effects of the sowing date on growth, development, and yield of maize, and to quantify the impact of light-temperature potential productivity on different stages of the yield formation. The results showed that delayed sowing decreased the vegetative growth period (VGP) and increased the reproductive growth period (RGP) of maize. The light-temperature potential productivity of the whole growth (WG) period had an exponential relationship with the theoretical yield. At least 14,614.95 kg ha⁻¹ of light-temperature potential productivity was needed to produce grain yield. The maximum theoretical yield was approximately 18,052.56 kg ha⁻¹, as indicated by the curve simulation results. The influence of light-temperature potential productivity on theoretical yield was as follows: VGP > RGP > vegetative and reproductive period (VRP). Accordingly, a method for determining the sowing time window based on VGP was established, and the optimal sowing dates were estimated for 1995–2021 and the SSP2-4.5 scenario in CMIP6 in the middle of this century (2030–2060). The simulation results showed that the optimum sowing date of maize “Lianyu 1” at the study site was 20–25 May in 1995–2021. In the middle of this century, the optimal sowing time of maize was ahead of schedule and the suitable sowing window was increased slightly. We conclude that advancing the sowing date of maize is a practical strategy for enhancing yield in the context of climate warming, and this strategy will provide a meaningful reference for scientific optimization of sowing dates to adapt maize to climate change.

The impact of changing meteorological conditions on crop quality has become a trending topic in current agriculture research. In this study, we analyzed the combined effects of both meteorological conditions and key stages on the nutritional quality of maize based on the data of field-staged sowing trials from 2018 to 2022. The results are as follows: (1) The key stage of meteorological conditions affecting the content of major nutritional qualities of maize is from 6 d before to 35 d after flowering. (2) The maximum temperature from 6 d before to 8 d after flowering, average temperature from 5 d before to 20 d after flowering, and minimum temperature from 9–20 d after flowering have significant positive effects on protein, fat, and essential amino acids, respectively; the daily difference in temperature from 9 to 35 d and 24 to 35 d after flowering have a significant negative effect on crude fiber and essential amino acids, respectively, and the daily difference in temperature from 24 to 35 d and the minimum temperature from 5 d before to 35 d after flowering have a significant effect on non-essential amino acids. (3) When the maximum temperature during the key stage of nutritional quality is 31.2 °C, the average temperatures are 24.9 °C and 22.4 °C, the minimum temperature is 18.9 °C, and the daily difference in temperature is 15.0 °C, the contents could reach the optimal values of 9.66% (protein), 4.80% (fat), 4.97% (crude fiber), 40.39 g·kg⁻¹ (essential amino acids), and 58.96 g·kg⁻¹ (non-essential amino acids), respectively. The findings provide a basis for adjusting the sowing period to improve the nutritional quality of maize in the context of climate change.

In response to the new concept of the impact of total climate production factors on plant phenology, this study verifies the feasibility of simulating plant phenology and triggering thresholds based on total climatic production factors by using the phenological and meteorological observation data of S. krylovii plants from 1985 to 2018 at the Xilinhot National Climate Observatory of China Meteorological Administration. The results indicate that the total climate production factors have a significant impact on plant phenological changes and can be effectively utilized for simulating phenology and determining triggering thresholds. The mutation of cumulative climate production potential based on total climate production factors can effectively indicate the green-up date and the wilting date of S. krylovii plants, and their triggering thresholds depend on the parameters of climate resource changes and the characteristics of plant biology, which are (0.085, −5.363) and (0.086, −27.620), respectively. The cumulative climate production potential based on total climate production factors can effectively indicate the heading date of S. krylovii plants, and its triggering thresholds also depend on the parameters of climate resource changes and the characteristics of plant biology, which is (394.632, −38,026.268). Furthermore, the results support the viewpoint that abrupt changes to the climate determine the beginning and ending of plant growth, while the accumulative climate resources determine the other phenological dates. This study provides new ideas for the study of plant phenology.

In response to the new concept of the impact of total climate production factors on plant phenology, this study will verify the feasibility of simulating plant phenology and triggering thresholds based on total climatic production factors by using the phenological and meteorological observation data of S. krylovii plant from 1985 to 2018 at the Xilinhot National Climate Observatory of China Meteorological Administration. The results indicate that the total climate production factors influencing plant phenological changes can be well used for phenological simulation and its triggering thresholds. The mutation of cumulative climate production potential based on total climate production factors can effectively indicate the green-up date and the wilting date of S. krylovii plant, and their triggering thresholds depend on the parameters of climate resource change and plant biology, which are (0.085, –5.363) and (0.086, –27.620), respectively. The cumulative climate production potential based on total climate production factors can effectively indicate the heading date of S. krylovii plant, and its triggering thresholds also depends on the parameters of climate resource change and plant biology, which is (394.632，–38026.268). Furthermore, the results support the viewpoint that the climate abrupt changes determine the beginning and the ending of plant growth, while the accumulative climate resources determine the other phenological dates. This study provides new ideas for the study of plant phenology.

The effects of temperature, and photoperiod on autumn phenology are well established for many species. However, the impact of multiple environmental factors and their interactions on regulating autumn phenology remains insufficiently explored. A large-scale controlled experiment in an artificial climate chamber was conducted from April to October 2021 at the Hebei Gucheng Agricultural Meteorology National Observation and Research Station, Hebei Province. This study aimed to investigate the interactive effects of temperature [T1.5, (1.5 °C above the control), T2, (2 °C above the control)], photoperiod [LP, long photoperiod (4 h photoperiod above the control), SP, short photoperiod (4 h photoperiod below the control)], and nitrogen addition [LN, low nitrogen, (nitrogen at 5 g N·m⁻²·a⁻¹), MN, medium nitrogen, (nitrogen at 10 g N·m⁻²·a⁻¹), HN, high nitrogen, (nitrogen at 20 g N·m⁻²·a⁻¹), control for temperature and photoperiod was the mean monthly temperature and average photoperiod (14 h) from 1989–2020 for Stipa krylovii, while the control for nitrogen treatment was without nitrogen addition] on leaf senescence in Stipa krylovii. A three-way analysis of variance (ANOVA) revealed significant effects of temperature, photoperiod, and nitrogen addition on leaf senescence (p < 0.01), with effects varying across different levels of each factor. Increased temperature notably delayed leaf senescence, with delays averaging of 4.0 and 6.3 days for T1.5 and T2, respectively. The LP treatment advanced leaf senescence by an average of 4.0 days, while the SP treatment delayed it by an average of 6.2 days; nitrogen addition advanced leaf senescence, with the effect intensifying as nitrogen levels increased, resulting in average advancements of 1.5, 1.9, and 4.3 days for LN, MN, and HN, respectively. Additionally, we observed that temperature altered the sensitivity of leaf senescence to the photoperiod, diminishing the advancement caused by LP at 2 °C and amplifying the delay caused by SP. These findings underscore the differential impacts of these three factors on the leaf senescence of Stipa krylovii and provide critical insights into plant phenology in response to varying environmental conditions.