Faced with severe global shortage of water and soil resources, studies on the integrated effect of water and nitrogen on tomato cultivation are urgently needed for sustainable agriculture. Two successive greenhouse experiments with three irrigation regimes (1, 2/3, 1/3 full irrigation) and four nitrogen levels (1, 2/3, 1/3, 0 nitrogen) were conducted; plant growth, fruit yield and quality were surveyed; and comprehensive quality and net profit were evaluated. The results show that water and nitrogen deficit decreased plant growth, evapotranspiration and yield while increasing production efficiency and fruit comprehensive quality. An antagonism effect from water and nitrogen application was found in tomato yield, organic acid, solids acid ratio, vitamin C and lycopene, whereas synergistic impact was observed in total soluble solids content. Water deficit had more significant effect on tomato yield and fruit quality parameters compared with that of nitrogen deficiency. Synthesizing the perspectives of yield, quality, resource productivity, market price index and profits, 1/3 full irrigation and 2/3 full nitrogen was the best strategy and could be recommended to farmers as an effective guidance for tomato production.

Drought vulnerability characteristics and risk assessment form the basis of drought risk management. In this study, the standardized precipitation index (SPI) and drought damage rates (DDR) were combined to analyze drought vulnerability characteristics and drought risk in Southwest China (SC). The information distribution method was applied to estimate the probability density of the drought strength (DS) and the two-dimensional normal information diffusion method was used to construct the vulnerability relationships between DS and drought damage (DD). The risk was then evaluated by combining the probability function of the DS and the DD vulnerability curve. The results showed that the relationship between the DS and the DD was nonlinear in SC and its provinces. With the increase in DS, the degree of DD increased gradually, stabilized, or decreased toward the end. However, the vulnerability characteristics of the different provinces varied widely due to multiple risk-bearing bodies and abilities to resist disasters. The risk values obtained across the range of time scales of the SPI were not significantly different. The yielding probabilities will be reduced for the crop area by 10%, 30%, and 70% due to drought. Compared to a normal year in SC, the probability values were 16.04%, 10.29%, and 2.70%, respectively. These results have the potential to provide a reference for agricultural production and drought risk management.

Accurate assessment of agricultural drought risk is of strategic significance to ensure future grain production security in the main grain production areas of China. Agricultural drought risk assessment is based on drought vulnerability characteristics. In this study, firstly the drought thresholds were redefined by correlation analysis of drought strength based on the Standardized Precipitation Evapotranspiration Index (SPEI) and drought damage rates, then the information distribution and the two-dimensional normal information diffusion method were employed to establish the vulnerability curve between drought strength and drought damage rates. Finally, provincial drought risks and the conditional probabilities at different drought damage stages were obtained. The results show that the drought vulnerability curve was nonlinear. With the increase of drought strength, drought damage rates increased rapidly at the beginning, and after a small fluctuation locally, they no longer increased significantly and tended to be relative stable. The occurrence probabilities of agricultural drought risk presented great spatial differences, with the characteristics of high in the northern, moderate in the central and southwestern part, and lower in the southeastern provinces in the main grain production areas of China. The analysis of conditional probability showed that Hubei, Henan, and Jiangxi were the provinces most prone to drought-affected risk under the drought-induced condition; while Liaoning, Hunan, and Inner Mongolia were the ones most prone to lost harvest risk under the drought-induced or the drought-affected condition. The results could be used to provide guidance for drought risk management and to formulate appropriate plans by the relevant departments.

Ecosystem light use efficiency (ELUE) is generally defined as the ratio of gross primarily productivity (GPP) to photosynthetically active radiation (PAR), which is an important ecological indictor used in dry matter prediction. Herein, investigating the dynamics of ELUE and its controlling factors is of great significance for simulating ecosystem photosynthetic production. Using 35 site-years eddy covariance fluxes and meteorological data collected at 11 cropland sites globally, we investigated the dynamics of ELUE and its controlling factors in four agroecosystems with paddy rice, soybean, summer maize and winter wheat. A “U” diurnal pattern of hourly ELUE was found in all the fields, and daily ELUE varied with crop growth. The ELUE for the growing season of summer maize was highest with 0.92 ± 0.06 g C MJ⁻¹, followed by soybean (0.80 ± 0.16 g C MJ⁻¹), paddy rice (0.77 ± 0.24 g C MJ⁻¹) and winter wheat (0.72 ± 0.06 g C MJ⁻¹). Correlation analysis showed that ELUE positively correlated with air temperature (T_a), normalized difference vegetation index (NDVI), evaporative fraction (EF) and canopy conductance (g_c, except for paddy rice sites), while it negatively correlated with the vapor water deficit (VPD). Besides, ELUE decreased in the days after a precipitation event during the active growing seasons. The path analysis revealed that the controlling variables considered in this study can account for 73.7%, 85.3%, 75.3% and 65.5% of the total ELUE variation in the rice, soybean, maize and winter wheat fields, respectively. NDVI is the most confident estimators for ELUE in the four ecosystems. Water availability plays a secondary role controlling ELUE, and the vegetation productivity is more constrained by water availability than T_a in summer maize, soybean and winter wheat. The results can help us better understand the interactive influences of environmental and biophysical factors on ELUE.

Accurate estimation of evapotranspiration (ET) and transpiration (T) is crucial in enhancing irrigation schedules in agricultural ecosystems. A simplified canopy resistance ($r_s^c$) algorithm grounded in the Ball-Berry model was integrated into Penman–Monteith (PM)-based models and assessed in a humid-region kiwifruit orchard. The Shuttleworth–Wallace (SW) and Clumping (CL) models agreed well with eddy covariance ET (ET_EC) throughout the growth season (R² = 0.79 and 0.89, RRMSE = 0.73–0.75 and 0.22, at the sub-daily and daily scales), outperforming the Two-Patch (TP) and topography- and vegetation-based surface energy partitioning (TVET) models. SW and CL also best reproduced sap-flow-based T (T_SF) (R² = 0.72 and 0.71–0.72, RRMSE = 0.85–0.87 and 0.38–0.39), primarily due to their higher accuracy during the mid stage. In this ecosystem, canopy interception evaporation had only a limited influence on the simulation performance of the SW and CL models. ET and T were most sensitive to changes in $r_s^c$ and related environmental factors, including gross primary productivity (GPP), the empirical parameter (a₁), and soil water content (θ). The sensitivity of T to θ was higher during the early stage but lower during the mid and late stages due to seasonal drought. ET was more sensitive to θ than T, due to its direct effect on soil surface resistance ($r_s^s$). T simulated by TP, TVET, and CL models showed greater sensitivity to leaf area index (LAI) than SW, while contrasting T and soil evaporation (E) responses to LAI caused ET to remain relatively insensitive. Both ET and T were highly sensitive to net radiation (R_n), air temperature (T_a), and vapor pressure deficit (VPD), all of which directly affect the energy balance. Overall, integrating the simplified canopy resistance algorithm with PM-based models improves ET and T estimation in humid-region orchards, supporting more efficient water management.