Accurate estimation of reference crop evapotranspiration (ET₀) is essential for water resources planning and irrigation scheduling. However, the absence of solar radiation (R_s) data is a common problem affecting the estimation of ET₀. This study investigates the feasibility of employing temperature-based models to estimate R_s and proposes effective methodologies for obtaining more convenient and accurate ET₀ estimates. To evaluate the effectiveness of different approaches, authors compared nine empirical models (M1−M9) and three machine learning algorithms (RF, GRNN and ANN) for daily R_s estimation. This analysis utilized data from 339 national basic meteorological stations in China, spanning the period from 2001 to 2018. Subsequently, authors proposed two strategies for estimating daily ET₀ in regions where solar radiation data is limited or unavailable. The results showed that (1) temperature-based models exhibited satisfactory accuracy (R²> 0.6) for daily R_s estimation, with machine learning algorithms outperforming their empirical counterparts. The machine learning accuracies are ranked as follows: Artificial Neural Network (ANN) > Generalized Regression Neural Network (GRNN) > Random Forest (RF). And empirical models are ranked in descending order of accuracy: M9 > M8 > M6 > M7 > M5 > M2 > M3 > M1 > M4. The accuracies of twelve models in the four climatic zones are indicated as follows: the temperate continental zone (TCZ) > the temperate monsoon zone (TMZ) > the subtropical monsoon zone (SMZ) > the mountain plateau zone (MPZ). (2) The comprehensive assessment for nine empirical models indicates that the Hargreaves-Samani model (M1) is the most reliable for solar radiation estimation. Its estimated results are close to those of the other models, and the coefficient of variation of the parameters (0.10) is much lower than that of the other empirical models. Thus, combining the model with the nationally calibrated parameters computed by the Kriging interpolation method allows for reliable values of the daily solar radiation. (3) Machine learning techniques show variations in estimating daily ET₀ across different climate zones. The machine learning accuracies are ranked as ANN>GRNN>RF, and TCZ>TMZ>MPZ>SMZ in the four climate zones. (4) The accuracies of the two daily ET₀ estimation strategies, with or without actual Rs calibration, are very close. Both strategies provide accurate daily ET₀ estimates (R²>0.95) with an average R² improvement of only 0.39% for strategy I compared to strategy II. In conclusion, this study provides new ideas to address the scarcity of solar radiation data and highlight the potential of machine learning in ET₀ estimation. This approach can be effectively applied to reference crop evapotranspiration estimates in regions where solar radiation data is scarce.

To investigate the impact of climate change on the growth period and yield of irrigated spring maize in arid region of Northwest China, the study was conducted based on located observational experiment and climatic data from 1984 to 2022. The results showed that the temperature increased significantly during the entire growth period with a climate trend rate of 0.76℃·10y⁻¹ (P<0.01), and a significant rise in active accumulated temperature above 10℃, with a climate trend rate of 135.80℃·d·10y⁻¹ (P<0.01). Although there was no significant change in precipitation during the entire growth period, it increased significantly during the milky-maturity stage, with a climate trend rate of 4.50mm·10y⁻¹ (P<0.05). The sunshine duration increased significantly from 1984 to 2004, with a climate trend rate of 126.88h·10y⁻¹ (P<0.01), but decreased significantly in the last 19 years, with a climate trend rate of −109.38h·10y⁻¹ (P<0.01). The growth days of spring maize increased from 1984 to 2004, with a climatic trend rate of 9.86d·10y⁻¹ (P<0.01), but decreased significantly in the last 19 years, with a climatic trend rate of 7.39d·10y⁻¹. The length of sowing-seedling and seventh leaf-jointing was significantly negatively correlated with temperature, respectively. The length of sowing-seeding, third leaf-seventh leaf and silking-milky was significantly positively correlated (P<0.05) with precipitation, respectively. The length of different growth periods was significantly positively correlated (P<0.01) with sunshine duration, respectively. The yield of spring maize fluctuated and climatic yield was significantly negatively correlated (P<0.05) with precipitation. In summary, climate change has been unfavorable to spring maize growth under current irrigation methods in arid region of northwest China.

 Extreme climate change, which can cause agricultural problems, has become a global hot topic. In recent decades, the Beijing-Tianjin-Hebei region has experienced several extreme climate events that have had a significant impact on grain yields. This study evaluted the impact of climate change on grain yields in the Beijing-Tianjin-Hebei region from 1980 to 2020 using meteorological data and maize yield per unit area data at eight sites and selected 25 national meteorological stations. Four types of statistical methods were selected, including linear regression, inverse distance weighting interpolation, M-K test, and Pearson correlation, to analyze the characteristics of climate change (maximum, minimum and average temperature, and growing degree days) and its impacts on maize yields. The results revealed: (1) the growing degree days (GDD) and temperature indices such as extreme maximum temperature (TXx) and high temperature days (Htd) exhibited an upward trend over time, with increase rates of 58.31℃·d·10y⁻¹, 0.39℃·10y⁻¹ and 0.96d·10y⁻¹, respectively. However, the low temperature indices (extreme minimum temperature, low temperature days) tended to decrease with decrease rates of 0.28℃·10y⁻¹ and 2.8d·10y⁻¹, respectively. Mutation analysis indicated a higher mutation rate for high temperature indices compared to low temperature indices, indicating a clear warming trend in the Beijing-Tianjin-Hebei region from 1980 to 2020. (2) The spatial distribution of extreme temperature index terms was different. High value of high temperature indices were primarily concentrated in economically developed cities such as Beijing and Tianjin, while low temperature indices were mainly concentrated in the northern (Zhangbei) and southwestern (Xingtai) areas. (3) The grain yield presented a fluctuate increase, with climate yield of maize fluctuating greatly (−1179 to 831kg·ha⁻¹). There were three climatic bumper years (2004, 2005 and 2006) and two lean years (1999, 2000) from 1990 to 2020 in the study area. Correlation analysis indicated that GDD、TXx、Htd were the primary response indices for grain yields in the Beijing-Tianjin-Hebei region, it can be seen that when TXx≥36℃, Htd≥4d, the climatic yield of maize decreases gradually. 

Soil denitrification is an important pathway of soil nitrogen loss. The large amount of nitrogen fertilizer applied in agricultural production leads to the increase of soil N₂O emission, and it has caused environmental problems such as the enhanced greenhouse effect. At the same time, soil denitrification is also the main way of N₂O reduction, so controlling the ratio of soil denitrification products N₂O/(N₂O+N₂) is the key to reduce soil N₂O emission. Based on a large quantity of relevant researches, this paper summarized the measurement methods and influencing factors of denitrification product ratio in soil and the mechanism of biochar addition affecting soil denitrification product ratio. The results showed that there was still existing uncertainty about the effect of biochar application on denitrification product ratio, and whether biochar addition could effectively regulate denitrification product ratio and reduce N₂O emission was affected by some influencing factors, such as soil physicochemical properties, biochar properties and application amount. Based on the above research status, the future prospects for improving the measurement methods of denitrification products and clarifying the quantitative effects of biochar addition denitrification products ratio and its key influencing factors were proposed.

Daily meteorological data from 1981 to 2022 and citrus growth periods data were used in this paper. The daily mean temperature, daily maximum temperature and their duration were selected as indicators of heat stress in citrus to clarify the spatial distribution characteristic and temporal variation trend of heat stress during citrus growth periods. The spatial and temporal differences of heat stress in different growing periods of citrus were compared using a paired t-test. The results show that (1) the average frequency, times and intensity of heat stress during flowering-fruit expanding stage of citrus are 87.73%, 2.23times·y⁻¹ and 5.28d·times⁻¹, respectively. High-value areas are mainly concentrated in the southeast of Jiangxi province. The average frequency, times and intensity of heat stress during the fruit expanding-fruit coloring stage of citrus are 56.43%, 1.15times·y⁻¹ and 2.74d·times⁻¹, respectively. High-value areas are mainly in the northern and central parts of Jiangxi province. (2) The station ratios, times and intensity of heat stress during the flowering-fruit expanding stage of citrus show a decreasing trend, with an average decrease of 0.90 percent points, 0.02 times and 0.06 d·time⁻¹ every 10 years, respectively. The station ratios, times and intensity of heat stress during fruit expanding-fruit coloring stage of citrus show an increasing trend, with an average increase of 6.80 percent points, 0.26times and 0.50d·times⁻¹ every 10 years, respectively. (3) The paired t-test results show that, the frequency, times and intensity of heat stress during flowering-fruit expanding stage of citrus are significantly higher than that during fruit expanding-fruit coloring stage of citrus. However, the climate tendency of times and intensity of heat stress during flowering-fruit expanding stage of citrus are significantly lower than that during fruit expanding-fruit coloring stage of citrus. The flowering-fruit expanding stage of citrus in Jiangxi province is the most frequent period of heat stress. For now, it is necessary to strengthen citrus defenses against heat stress during the flowering-fruit expanding stage. In the future, we should improve the prevention of heat stress in citrus during the fruit expanding-fruit coloring stage.

Heat events were identified based on the daily maximum temperature data at 27 meteorological stations located in the Beijing-Tianjin-Hebei region from 1960 to 2019. The Copula function was introduced to fit the joint cumulative probability distribution of the two characteristics, taking into account the duration of heat events and the heat intensity (defined as ≥35℃ accumulated temperature), so as to obtain the joint return period of the heat events with arbitrary duration and heat intensity and analyze the return period characteristics of heat events in the Beijing-Tianjin-Hebei region. The results demonstrate that the distribution of the frequency and severity of heat events in the Beijing-Tianjin-Hebei region is higher in the south and lower in the north, but the increase in frequency, duration and average daily intensity is lower in the south and higher in the north; the effect of POISS function is optimal at all sites by fitting the edge distribution of the duration of heat events; when fitting the heat intensity, the GEV function works more efficiently at a greater number of station; when combining the duration and intensity of heat events in two dimensions, the Copula function used most is Symmetrised Joe-Clayton function, followed by Frank function; return period of heat events lasting more than five days in the study area exceeds once in five years, and once in 100 years in the northern region. The results of the study can provide a reference for heat disaster prevention and mitigation in the Beijing-Tianjin-Hebei region.