N-(4-Chloro­phen­yl)-4-meth­oxy-3-(propanamido)­benzamide cyclo­hexane hemisolvate

The title compound, C17H17ClN2O3·0.5C6H12, was prepared by the condensation reaction of 4-meth­oxy-3-(propanamido)­benzoic acid with 4-chloro­aniline. The Cl atom, the propionyl CH3 group and the cyclo­hexyl CH2 group are disordered over two sets of sites of equal occupancy in both mol­ecules. The cyclo­hexane solvent mol­ecule is disordered over two orientations which were modelled with relative occupancies of 0.484 (4) and 0.516 (4). In the crystal, there are a number of N—H⋯O hydrogen bonds, forming layers perpendicular to (001)

Development of a new ferulic acid certified reference material for use in clinical chemistry and pharmaceutical analysis

This study compares the results of three certified methods, namely differential scanning calorimetry (DSC), the mass balance (MB) method and coulometric titrimetry (CT), in the purity assessment of ferulic acid certified reference material (CRM). Purity and expanded uncertainty as determined by the three methods were respectively 99.81%, 0.16%; 99.79%, 0.16%; and 99.81%, 0.26% with, in all cases, a coverage factor (k) of 2 (P=95%). The purity results are consistent indicating that the combination of DSC, the MB method and CT provides a confident assessment of the purity of suitable CRMs like ferulic acid

Polymorphs and Transformations of the Solid Forms of Organic Salts of 5-Sulfosalicylic Acid and Isonicotinamide

Diversities of salts formed by 5-sulfosalicylic acid (5-SSA) and isonicotinamide (INA) were obtained by mechanochemical grinding and from solution, including three polymorphs of (5-SSA-2H)–·(INA-H)+·H2O (1) (form 1a, form 1b, and form 1c), dehydration phase (5-SSA-2H)2–·(INA-H)2+ (2), solvent phases (5-SSA-2H)–·(INA-H)+·MeOH (3) and (5-SSA-2H)2–·(INA-H)2+·H2O·MeOH (4), and stoichiometric form (5-SSA-2H)–·(INA-H)+·(INA) (5). The work studies not only the stabilities of the three polymorphs and the relationship between solvated 1 and desolvation phase 2 but also the interconversion of these crystalline phases. The dehydrated phase 2 and solvent phase 4 comprising two independent 5-SSA anions and two independent INA cations in the asymmetric unit form an individual ion pair and the corresponding hydrogen bonded networks. The solvent phase (5-SSA-2H)2–·(INA-H)2+·H2O·MeOH (4) shows the individual structural features of both (5-SSA-2H)–·(INA-H)+·H2O (1) and solvent phase (5-SSA-2H)–·(INA-H)+·MeOH (3). All seven salts build up layered structures but comprising a different hydrogen bonding network and can experience a reversible transformation. Quantum mechanics calculations are used to assess the stability of some of the crystalline phases under investigation. The example of salts of 5-SSA and INA comprising a diversity of solid forms reported here can be considered very rare, which has provided a good model for the insight into the structural transformation of materials of interest

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Complete and accurate global solar radiation (Rs) data at a specific region are crucial for regional climate assessment and crop growth modeling. The objective of this paper was to evaluate the capability of 12 solar radiation models based on meteorological data obtained from 21 meteorological stations in China. The results showed that the estimated and measured daily Rs had statistically significant correlations (P<0.01) for all the 12 models in 7 subzones of China. The Bahel model showed the best performance for daily Rs estimation among the sunshine-based models, with average R2 of 0.910, average RMSE of 2.306 MJ m−2 d−1, average RRMSE of 17.3%, average MAE of 1.724 MJ m−2 d−1, and average NS of 0.895, respectively. The Bristow-Campbell (BC) model showed the best performance among the temperature-based models, with average R2 of 0.710, average RMSE of 3.952 MJ m−2 d−1, average RRMSE of 29.5%, average MAE of 2.958 MJ m−2 d−1, and average NS of 0.696, respectively. On monthly scale, Ögelman model showed the best performance among the sunshine-based models, with average RE of 5.66%. The BC model showed the best performance among the temperature-based models, with average RE of 8.26%. Generally, the sunshine-based models were more accurate than the temperature-based models. Overall, the Bahel model is recommended to estimate daily Rs, Ögelman model is recommended to estimate monthly average daily Rs in China when the sunshine duration is available, and the BC model is recommended to estimate both daily Rs and monthly average daily Rs when only temperature data are available

Genetic Algorithm-Optimized Extreme Learning Machine Model for Estimating Daily Reference Evapotranspiration in Southwest China

Reference evapotranspiration (ET0) is an essential component in hydrological and ecological processes. The Penman&ndash;Monteith (PM) model of Food and Agriculture Organization of the United Nations (FAO) model requires a number of meteorological parameters; it is urgent to develop high-precision and computationally efficient ET0 models with fewer parameter inputs. This study proposed the genetic algorithm (GA) to optimize extreme learning machine (ELM), and evaluated the performances of ELM, GA-ELM, and empirical models for estimating daily ET0 in Southwest China. Daily meteorological data including maximum temperature (Tmax), minimum temperature (Tmin), wind speed (u2), relative humidity (RH), net radiation (Rn), and global solar radiation (Rs) during 1992&ndash;2016 from meteorological stations were used for model training and testing. The results from the FAO-56 Penman&ndash;Monteith formula were used as a control group. The results showed that GA-ELM models (with R2 ranging 0.71&ndash;0.99, RMSE ranging 0.036&ndash;0.77 mm&middot;d&minus;1) outperformed the standalone ELM models (with R2 ranging 0.716&ndash;0.99, RMSE ranging 0.08&ndash;0.77 mm&middot;d&minus;1) during training and testing, both of which were superior to empirical models (with R2 ranging 0.36&ndash;0.91, RMSE ranging 0.69&ndash;2.64 mm&middot;d&minus;1). ET0 prediction accuracy varies with different input combination models. The machine learning models using Tmax, Tmin, u2, RH, and Rn/Rs (GA-ELM5/GA-ELM4 and ELM5/ELM4) obtained the best ET0 estimates, with R2 ranging 0.98&ndash;0.99, RMSE ranging 0.03&ndash;0.21 mm&middot;d&minus;1, followed by models with Tmax, Tmin, and Rn/Rs (GA-ELM3/GA-ELM2 and ELM3/ELM2) as inputs. The machine learning models involved with Rn outperformed those with Rs when the quantity of input parameters was the same. Overall, GA-ELM5 (Tmax, Tmin, u2, RH and Rn as inputs) outperformed the other models during training and testing, and was thus recommended for daily ET0 estimation. With the estimation accuracy, computational costs, and availability of input parameters accounted, GA-ELM2 (Tmax, Tmin, and Rs as inputs) was determined to be the most effective model for estimating daily ET0 with limited meteorological data in Southwest China

Deficit drip irrigation improves kiwifruit quality and water productivity under rain-shelter cultivation in the humid area of South China

Comprehending crop responses to water deficit at different growth stages is crucial for developing effective irrigation strategies. Different water deficit treatments (WDTs) were applied to the kiwifruit vines to investigate the effect of water deficit during different growth stages on the fruit quality, yield, and water productivity (WP); subsequently, the technique for order preference by similarity to an ideal solution method (TOPSIS) was employed to determine optimal treatments for kiwifruit cultivation. A total of 17 irrigation treatments were applied, including one control treatment (CTL, full irrigation) and four WDTs (denoted as D15%, D25%, D35%, and D45% respectively) during the bud burst to leafing (I), flowering to fruit set (II), fruit expansion (III), and fruit maturation (IV) stages. Results showed that WDTs during I, II, III, and IV decreased evapotranspiration (ET) over the whole growth period of kiwifruit vines by 1.2–3.8, 1.5–4.4, 4.7–14.3, and 6.9–21.3% compared with CTL, respectively. WDTs during stages I and II increased fruit volume (Vf) and fruit weight (FW), while exhibiting no significant impact on yield, WP, and chemical quality of kiwifruit. WDTs during stage III improved fruit firmness (Fn), total soluble solids (TSS), and titratable acidity (TA); however, it also caused severe reduction in Vf, FW, yield, and WP. Appropriate WDTs during stage IV significantly improved Fn, TSS, TA, vitamin C (Vc), and WP without compromising Vf, FW, and yield of kiwifruit. The IV-D25% treatment was determined to be the optimal treatment for improving fruit quality and WP of kiwifruit while maintaining yield, which increased TSS, TA, Vc, and WP by 9.1, 6.1, 19.2, 4.6%, respectively; the combination of D25%, D25%, full irrigation, and D25% treatments during stages I, II, III, and IV should be a viable irrigation strategy to simultaneously achieve high yield, quality, and WP of kiwifruit

Optimizing nitrogen fertilizer application for achieving high yield with low environmental risks in apple orchard

The great challenges of food security, climate change and environmental degradation resulting from unreasonable nitrogen fertilizer (NF) inputs necessitate the optimization of fertilizer application to ensure fruit production and environmental sustainability. However, there is a lack of systematic analysis on the effects of NF inputs on apple yield and reactive nitrogen losses (N2O emission, NH3 volatilization, and nitrate leaching) at the global scale. Therefore, we used a meta-analysis of 159 observations from 31 published studies to evaluate the response of apple yield and reactive nitrogen losses to NF. We aim to identify suitable NF rates, environmental conditions, and planting factors that improve apple yield while minimizing reactive nitrogen losses. Our results showed that NF significantly increased apple yield, N2O emission, NH3 volatilization, and nitrate leaching by 17.1%, 255.7%, 236.4%, and 68.7% compared with no nitrogen fertilizer (NNF), respectively. The effects of NF rates and environmental factors on the response of N2O emission, NH3 volatilization, and nitrate leaching to NF were prior to those of planting factors, but the result of apple yield was the opposite. Apple production under NF in regions with MAT (mean annual air temperature) ≥ 10 °C and MAP (mean annual precipitation) 450 kg ha−1). Our findings provide guidance for optimizing NF management in apple orchard to achieve high crop yields, reduce emissions, and mitigate pollution

Investigating the Patterns and Controls of Ecosystem Light Use Efficiency with the Data from the Global Farmland Fluxdata Network

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 (Ta), normalized difference vegetation index (NDVI), evaporative fraction (EF) and canopy conductance (gc, 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 Ta in summer maize, soybean and winter wheat. The results can help us better understand the interactive influences of environmental and biophysical factors on ELUE

Investigating the Patterns and Controls of Ecosystem Light Use Efficiency with the Data from the Global Farmland Fluxdata Network

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 (Ta), normalized difference vegetation index (NDVI), evaporative fraction (EF) and canopy conductance (gc, 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 Ta in summer maize, soybean and winter wheat. The results can help us better understand the interactive influences of environmental and biophysical factors on ELUE

Genetic Algorithm-Optimized Extreme Learning Machine Model for Estimating Daily Reference Evapotranspiration in Southwest China

Reference evapotranspiration (ET0) is an essential component in hydrological and ecological processes. The Penman–Monteith (PM) model of Food and Agriculture Organization of the United Nations (FAO) model requires a number of meteorological parameters; it is urgent to develop high-precision and computationally efficient ET0 models with fewer parameter inputs. This study proposed the genetic algorithm (GA) to optimize extreme learning machine (ELM), and evaluated the performances of ELM, GA-ELM, and empirical models for estimating daily ET0 in Southwest China. Daily meteorological data including maximum temperature (Tmax), minimum temperature (Tmin), wind speed (u2), relative humidity (RH), net radiation (Rn), and global solar radiation (Rs) during 1992–2016 from meteorological stations were used for model training and testing. The results from the FAO-56 Penman–Monteith formula were used as a control group. The results showed that GA-ELM models (with R2 ranging 0.71–0.99, RMSE ranging 0.036–0.77 mm·d−1) outperformed the standalone ELM models (with R2 ranging 0.716–0.99, RMSE ranging 0.08–0.77 mm·d−1) during training and testing, both of which were superior to empirical models (with R2 ranging 0.36–0.91, RMSE ranging 0.69–2.64 mm·d−1). ET0 prediction accuracy varies with different input combination models. The machine learning models using Tmax, Tmin, u2, RH, and Rn/Rs (GA-ELM5/GA-ELM4 and ELM5/ELM4) obtained the best ET0 estimates, with R2 ranging 0.98–0.99, RMSE ranging 0.03–0.21 mm·d−1, followed by models with Tmax, Tmin, and Rn/Rs (GA-ELM3/GA-ELM2 and ELM3/ELM2) as inputs. The machine learning models involved with Rn outperformed those with Rs when the quantity of input parameters was the same. Overall, GA-ELM5 (Tmax, Tmin, u2, RH and Rn as inputs) outperformed the other models during training and testing, and was thus recommended for daily ET0 estimation. With the estimation accuracy, computational costs, and availability of input parameters accounted, GA-ELM2 (Tmax, Tmin, and Rs as inputs) was determined to be the most effective model for estimating daily ET0 with limited meteorological data in Southwest China