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Reasonable dense planting is an important measure to increase crop yield per unit area and save resources. However, there is no unified view of the competition for photosynthetic radiation in different stratification structures of maize plants due to different planting densities, as well as the internal mechanism of yield increase. In order to investigate these issues, field experiments were conducted from 2021 to 2022 in Daxing, Beijing, China (39°37′ N, 116°25′ E, altitude 31.3 m a.s.l.). Field plots were arranged in a randomized block design, with the main plot factor representing plant density. In each replicate, four densities were set, i.e., 33,000 (D1), 42,000 (D2), 55,000 (D3), and 83,000 (D4) plants·ha−1. Canopy stratification structure characteristics, including leaf area index, biomass, and photosynthetically active radiation (PAR), were measured in each stratification structure, and transmitted PAR, radiation use efficiency (RUE), and light extinction coefficient (K) were calculated. We found that increasing plant density significantly increased biomass, leaf area index (LAI), and precipitation use efficiency (PUE), but the light extinction coefficient (K) and harvest index (HI) showed opposite trends. Compared to the D1 treatment, the grain yield, precipitation use efficiency (PUE), radiation use efficiency (RUE), and LAI increased by 22.6–88.2%, 9.2–50.5%, 26.7–116.9%, and 27.7 to 150.6% in the D2, D3, and D4 treatments, and K and HI decreased by 19.7–50.3% and 4.2–11.5%, respectively. These showed that a density of 83,000 plants·ha−1 was effective in promoting maize growth in the Daxing area of Beijing, China. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation challenges. Moreover, the existing LST products are constrained by their low spatiotemporal resolution, limiting their broader applicability. The fusion of multi-source remote sensing data offers a viable solution to enhance spatiotemporal resolution. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used to estimate time series LST utilizing multi-temporal Landsat 8 (L8) and MOD21A2 within the Haihe basin in 2021. Validation of ESTARFM LST was conducted against L8 LST and in situ LST. The results can be summarized as follows: (1) ESTARFM was found to be effective in heterogeneous regions within the Haihe basin, yielding LST with a spatiotemporal resolution of 30 m and 8 d while retaining clear texture information; (2) the comparison between ESTARFM LST and L8 LST shows a coefficient determination (R2) exceeding 0.59, a mean absolute error (MAE) lower than 2.43 K, and a root mean square error (RMSE) lower than 2.63 K for most dates; (3) comparison between ESTARFM LST and in situ LST showcased high validation accuracy, revealing a R2 of 0.87, a MAE of 2.27 K, and a RMSE of 4.12 K. The estimated time series LST exhibited notable reliability and robustness. This study introduced ESTARFM for LST estimation, achieving satisfactory outcomes. The findings offer a valuable reference for other regions to generate LST data with a spatiotemporal resolution of 8 d and 30 m, thereby enhancing the application of data products in agriculture and hydrology contexts. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area. [ABSTRACT FROM AUTHOR]

The Turpan basin is one of the most arid and water-insecure regions in China. Turpan Basin has a continental desert climate as it is located in central Eurasia and surrounded by mountains on all sides, with the lowest elevation of 154.5 m below sea level. Its rivers and water availability are mainly supplied by mountain snowmelt through surface streams and the Karez system. To assess the impact of climate change on streamflow in the Turpan basin, this study examined the long-term trends and change points of hydro-meteorological variables and explored the plausible correlation between them at annual and seasonal scales. A set of non-parametric statistical tests was applied to analyze the trend, its magnitude, and the change point of the hydro-meteorological variables from 1959 to 2012 in the Turpan basin. Results showed that the annual mean temperature had a significant increasing trend with an average warming rate of 0.26 °C/decade. Annual precipitation did not show a consistent upward or downward trend across the basin; however, there was a significant increase in winter precipitation. The increasing rates of streamflow at Alagou and Meiyaogou hydrological stations were the highest in the summer (0.42 and 0.18 m3/s per decade, respectively), which might increase the risk of flooding. The change points of various meteorological variables and streamflow can be generally found in the 1980s, in which temperature, precipitation, relative humidity, and streamflow had abrupt changes concurrently in 1986 and had respectively increased by 7.2, 41.3, 5.7, and 18.3% after 1986. In general, the strength of the relationship between annual streamflow and precipitation (0.34) was greater than that between streamflow and temperature (0.16). The significant increase in temperature and agriculture in the basin in recent decades indicates the importance of reducing unnecessary soil evaporation in agriculture to preserve water resources under climate change and using innovative technology to preserve soil quality under increasing evapotranspiration and irrigation. This study provided valuable information for understanding the changing properties of the streamflow in the basin and insights for better-integrated water resources management planning that focuses on mitigating flood disasters and soil degradation and improving irrigation efficiency. [ABSTRACT FROM AUTHOR]

Enzymatic polyethylene terephthalate (PET) recycling processes are gaining interest for their low environmental impact, use of mild conditions, and specificity. Furthermore, PET hydrolase enzymes are continuously being discovered and engineered. In this work, we studied a PET hydrolase (PET2), initially characterized as an alkaline thermostable lipase. PET2 was produced in a fusion form with a 6-histidine tag in the N-terminal. The PET2 activity on aromatic terephthalate and new indole-based polyesters was evaluated using polymers in powder form. Compared with IsPETase, an enzyme derived from Ideonella sakaiensis, PET2 showed a lower PET depolymerization yield. However, interestingly, PET2 produced significantly higher polybutylene terephthalate (PBT) and polyhexylene terephthalate (PHT) depolymerization yields. A clear preference was found for aromatic indole-derived polyesters over non-aromatic ones. No activity was detected on Akestra™, an amorphous copolyester with spiroacetal structures. Docking studies suggest that a narrower and more hydrophobic active site reduces its activity on PET but favors its interaction with PBT and PHT. Understanding the enzyme preferences of polymers will contribute to their effective use to depolymerize different types of polyesters. [ABSTRACT FROM AUTHOR]

Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due to its characteristics, which cannot be measured directly. For the exact assessment of the Li-ion battery state of charge, the research proposes an extreme learning machine algorithm based on the alternating factor multiplier method with improved regularization. This method constructs a suitable online Li-ion battery state of charge prediction model using the alternating factor multiplier method in gradient form. The experiment demonstrates that the algorithm in the study has a reduction in the number of nodes in the implicit layer relative to the traditional extreme learning machine algorithm. The error fluctuations of the algorithm under two different excitation functions range from [−0.005, 0.005] and [0.082, 0.265]; The root mean square error of the data set in which the algorithm performs well is 1.9516 and 0.6157, respectively. The real simulation scenario created the predicted values of the state of charge in the realistic simulation scenario that fit the real value curve by 99.99%. The average and maximum errors of the proposed state of charge prediction model are the smallest compared to the long and short-term memory networks and gated cyclic units, 0.58% and 2.97%, respectively. The experiment demonstrates that the presented algorithm can reduce the computational burden while guaranteeing the state of charge model prediction. [ABSTRACT FROM AUTHOR]

Featured Application: The potential use of bacteria and their enzymes for the degradation of plastic waste and the recovery of value-added products considering bio-up recycling for the circular economy. A polyethylene terephthalate (PET)-degrading bacterium identified as Stenotrophomonas maltophilia PRS8 was isolated from the soil of a landfill. The degradation of the PET bottle flakes and the PET prepared as a powder were assessed using live cells, an extracellular medium, or a purified cutinase-like enzyme. These treated polymers were analyzed using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The depolymerization products, identified using HPLC and LC-MS, were terephthalic acid (TPA), mono(2-hydroxyethyl)-TPA (MHET), and bis(2-hydroxyethyl)-TPA (BHET). Several physicochemical factors were optimized for a better cutinase-like enzyme production by using unique single-factor and multi-factor statistical models (the Plackett–Burman design and the central composite design software). The enzyme was purified for homogeneity through column chromatography using Sephadex G-100 resin. The molecular weight of the enzyme was approximately 58 kDa. The specific activity on para nitrophenyl butyrate was estimated at 450.58 U/mg, with a purification of 6.39 times and a yield of 48.64%. The enzyme was stable at various temperatures (30–40 °C) and pH levels (8.0–10.0). The enzyme activity was significantly improved by the surfactants (Triton X-100 and Tween-40), organic solvent (formaldehyde), and metals (NiCl2 and Na2SO4). The extracellular medium containing the cutinase-type enzyme showed a depolymerization yield of the PET powder comparable to that of Idonella skaiensis IsPETase and significantly higher than that of Humicola insolens thermostable HiCut (HiC) cutinase. This study suggests that S. maltophilia PRS8 is able to degrade PET at a mesophilic temperature and could be further explored for the sustainable management of plastic waste. [ABSTRACT FROM AUTHOR]

Unreasonable fertilizer and irrigation applications and dosages in orchards in northern China result in poor vegetative growth and fruit quality. To reveal the combined effect of water and fertilizer coupling on vegetative growth and fruit quality, this study used pear as a field experiment material, considering: (1) irrigation lower limits (55%, 65%, 75%θf, θf is field capacity) and (2) nitrogen fertilizer application (162, 324, 486 kg·ha−1). Nine coupling schemes and a control treatment (C) were set up in the orthogonal combination. The results showed that, under the higher irrigation rate and nitrogen dose, the spring shoot length, base diameter, and leaves relative chlorophyll content values were increased by 36.77%, 31.86% and 12.91%, respectively. The response of each coupling scheme was different. However, selected water and nitrogen coupling schemes improved the fruit quality. The evaluation results indicated that medium irrigation and high fertilizer scheme were optimal. In conclusion, integrating the vegetative growth and fruit quality, it is recommended that the water and fertilizer coupling scheme for pear in the northern China is as follows: a lower irrigation limit of 65%θf and a nitrogen fertilizer amount of 486.00 kg·ha−1. [ABSTRACT FROM AUTHOR]

Early forecasting of crop yield from field to region is important for stabilizing markets and safeguarding food security. Producing a precise forecasting result with fewer inputs is an ongoing goal for the large-area yield evaluation. We present one approach of yield prediction for maize that was explored by incorporating remote-sensing-derived land surface temperature (LST) and field in-season data into a series of logistic models with only a few parameters. Continuous observation data of maize were utilized to calibrate and validate the corresponding logistic models for regional biomass estimating based on field temperatures (including crop canopy temperature (Tc)) and relative dry/fresh biomass accumulation. The LST maps from MOD11A1 products, which are considered to be matched as Tc in large irrigation districts, were assimilated into the validated models to estimate the biomass accumulation. It was found that the temporal-scale difference between the instantaneous LST and the daily average value of field-measured Tc was eliminated by data normalization method, indicating that the normalized LST could be input directly into the model as an approximation of the normalized Tc. Making one observed biomass in-season as the driving force, the maximum of dry/fresh biomass accumulation (DBA/FBA) at harvest could be estimated. Then, grain yield forecasting could be achieved according to the local harvest index of maize. Silage and grain yields were evaluated reasonably well compared with field observations based on the regional map of LST values obtained in 2017 in Changchun, Jilin Province, China. Here, satisfactory grain and silage yield forecasting was provided by assimilating once measured value of DBA/FBA at the middle growth period (early August) into the model in advance of harvest. Meanwhile, good results were obtained in the application of this approach using field data in 2016 to predict grain yield ahead of harvest in the Jiefangzha sub-irrigation district, Inner Mongolia, China. This study demonstrated that maize yield can be forecasted accurately prior to harvest by assimilating remote-sensing-derived LST and field data into the logistic models at a regional scale considering the spatio-temporal scale extension of ground information and crop dynamic growth in real time. [ABSTRACT FROM AUTHOR]

A hydrogel is a three-dimensional structure that holds plenty of water, but brittleness largely limits its application. Self-healing hydrogels, a new type of hydrogel that can be repaired by itself after external damage, have exhibited better fatigue resistance, reusability, hydrophilicity, and responsiveness to environmental stimuli. The past decade has seen rapid progress in self-healing hydrogels. Self-healing hydrogels can automatically self-repair after external damage. Different strategies have been proposed, including dynamic covalent bonds and reversible noncovalent interactions. Compared to traditional hydrogels, self-healing gels have better durability, responsiveness, and plasticity. These features allow the hydrogel to survive in harsh environments or even to be injected as a drug carrier. Here, we summarize the common strategies for designing self-healing hydrogels and their potential applications in clinical practice. [ABSTRACT FROM AUTHOR]

Land surface model is a powerful tool for estimating continuous soil water content (SWC) and surface fluxes. However, simulation error tends to accumulate in the process of model simulation due to the inevitable uncertainties of forcing data and the intrinsic model errors. Data assimilation techniques consider the uncertainty of the model, update model states during the simulation period, and therefore improve the accuracy of SWC and surface fluxes estimation. In this study, an Ensemble Kalman Filter (EnKF) technique was coupled to a Hydrologically Enhanced Land Process (HELP) model to update model states, including SWC and surface temperature (Ts). The remotely sensed latent heat flux (LE) estimated by Surface Energy Balance System (SEBS) was used as the observation value in the data assimilation system to update the model states such as SWC and Ts, etc. The model was validated by the observation data in 2006 at the Weishan flux station, where the open-loop estimation without state updating was treated as the benchmark run. Results showed that the root mean square error (RMSE) of SWC was reduced by 30%~50% compared to the benchmark run. Meanwhile, the surface fluxes also had significant improvement to different extents, among which the RMSE of LE estimation from the wheat season and maize season reduced by 33% and 44%, respectively. The application of the data assimilation technique can substantially improve the estimation of surface fluxes and SWC states. It is suggested that the data assimilation system has great potential to be used in the application of land surface models in agriculture and water management. [ABSTRACT FROM AUTHOR]

Accurate description of surface soil moisture (SSM) in vegetation-covered areas is of great significance to water resource management and drought monitoring. To remove the effect of vegetation on SSM estimation, the vegetation index obtained from Sentinel-2 (S2) was applied for vegetation water content (VWC) estimation. The VWC model was substituted into the water cloud model (WCM), and thus, the SSM estimation model was developed based on the WCM. The methodology was tested at Daxing, Beijing, and Gu'an, Hebei, in which training and validation data of SSM were acquired by in situ measurements. The results can be described as follows: (1) For the vegetation-covered areas, the Modified Chlorophyll Absorption Ratio Index (MCARI) obtained from the B3, B4, and B5 bands of S2 was the most suitable for removing the influence of vegetation on SSM estimation; (2) Compared to Sentinel-1 (S1) vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization was more suitable for SSM estimation and achieved higher accuracy; (3) The developed model could be used to estimate SSM under crop cover with high accuracy, which indicated the correlation coefficients (R2) between in situ measured and estimated SSM were 0.867, the root mean square error (RMSE) was 0.028 cm3/cm3, and the MAE was 0.023 cm3/cm3. Thus, this methodology has the potential for SSM estimation in vegetated areas. [ABSTRACT FROM AUTHOR]

Soil salinization is a global problem that causes huge losses in agricultural production. Salt can interfere with crop absorption and metabolism of nutrients and water, affect plant physiological responses and reduce plant biomass. Maize, a very important economic crop, can adapt to a certain degree of saline-alkali soil. It is essential to understand the physiological indexes of response to soil salinity concentrations and explore the effects of different nitrogen fertilizer treatments on maize growth. In this study, three soil salinity gradients (S1, S2 and S3 were with soil salt concentration, Ssc, of 0, 0.1% and 0.25%, respectively) and two nitrogen application rates (N0 and N1 were without and with nitrogen applied (13.2 g per pot), respectively) were set up. Plant growth and photosynthetic parameters were measured. Whether nitrogen was applied or not, with the increase in Ssc, leaf area, plant height, stem diameter, SPAD, leaf water potential, RuBP carboxylase, and PEP carboxylase activities, photosynthetic rate (A), stomatal conductance (gs), the maximum stomatal conductance (gsmax), and the stomatal morphological parameters such as stomatal width and maximum stomatal area (amax), all showed a downward trend. Under the S1 and S2 treatments, compared with the N0, the N1 treatment alleviated the stress effect of the Ssc on these indicators. However, under S3 treatment, the stress degrees of leaf water potential, gs, gsmax and amax, were aggravated after nitrogen application. This indicated that under the high Ssc of S3, the interaction between nitrogen application and soil salinity should be considered. WUEin increased with the increase in Ssc. Moreover, under N1 treatments, the increase in WUEin with Ssc was greater than that with N0. With the increase in Ssc, whether nitrogen was applied or not, the dry weight of maize declined by 44.2% and 73.0%, respectively, for the S2 and S3 treatments. Under S2 treatment, N1 significantly improved the dry matter mass of maize compared with the N0 treatment. The results showed that soil salt stress can inhibit crop growth, physiology and dry matter accumulation, and that nitrogen application can alleviate this within a specific salinity range. Such results indicate that in saline-alkali areas, whether nitrogen fertilizer is applied or not should depend on the level of Ssc to improve plant growth. [ABSTRACT FROM AUTHOR]

Featured Application: Potential use of enzymes in the development of bioprocesses for the degradation and recycling of residual plastic fibers. Terephthalate polyesters such as poly(ethylene terephthalate) (PET) have been massively produced over the last few decades due to their attractive properties in multiple applications. However, due to their limited biodegradability, they have accumulated in landfills and oceans, posing an environmental threat. Enzymatic recycling technologies are predicted to generate long-term socioeconomic benefits. In the present work, we compared the IsPETase (from Ideonella sakaiensis 201-F6) activity on a series of polyesters, including poly(butylene) terephthalate (PBT), poly(hexamethylene) terephthalate (PHT) and Akestra™, with PET. The IsPETase showed remarkable activity toward PET (39% degradation of the original polyester) that was higher than that toward Akestra™ (0.13%), PBT (0.25%) and PHT (0.13%) after 72 h. Thus, based on experimental data and computational analysis, we report insights into IsPETase activity on a series of terephthalate-based polyesters. Aside from that, the fusion domain (Trx) effect in the production and activity of a recombinant Trx-IsPETase is reported. [ABSTRACT FROM AUTHOR]

Global climate change and the spread of COVID-19 have caused widespread concerns about food security. The development of smart agriculture could contribute to food security; moreover, the targeted and accurate management of crop nitrogen is a topic of concern in the field of smart agriculture. Unmanned aerial vehicle (UAV) spectroscopy has demonstrated versatility in the rapid and non-destructive estimation of nitrogen in summer maize. Previous studies focused on the entire growth season or early stages of summer maize; however, systematic studies on the diagnosis of nitrogen that consider the entire life cycle are few. This study aimed to: (1) construct a practical diagnostic model of the nitrogen life cycle of summer maize based on ground hyperspectral data and UAV multispectral sensor data and (2) evaluate this model and express a change in the trend of nitrogen nutrient status at a spatiotemporal scale. Here, a comprehensive data set consisting of a time series of crop biomass, nitrogen concentration, hyperspectral reflectance, and UAV multispectral reflectance from field experiments conducted during the growing seasons of 2017–2019 with summer maize cultivars grown under five different nitrogen fertilization levels in Beijing, China, were considered. The results demonstrated that the entire life cycle of summer maize was divided into four stages, viz., V6 (mean leaf area index (LAI) = 0.67), V10 (mean LAI = 1.94), V12 (mean LAI = 3.61), and VT-R6 (mean LAI = 3.94), respectively; moreover, the multi-index synergy model demonstrated high accuracy and good stability. The best spectral indexes of these four stages were GBNDVI, TCARI, NRI, and MSAVI2, respectively. The thresholds of the spectral index of nitrogen sufficiency in the V6, V10, V12, VT, R1, R2, and R3–R6 stages were 0.83–0.44, −0.22 to −5.23, 0.42–0.35, 0.69–0.87, 0.60–0.75, 0.49–0.61, and 0.42–0.53, respectively. The simulated nitrogen concentration at the various growth stages of summer maize was consistent with the actual spatial distribution. [ABSTRACT FROM AUTHOR]

Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of PGs, of which many map PGs based on spectral responses and geometric shapes. In this study, we proposed a new fine- and coarse-scale mapping approach using two-temporal Sentinel-2 images with various seasonal characteristics and a one-dimensional convolutional neural network (1D-CNN). Having applied this approach in a pilot area study, the results were summarized as follows: (1) A time-series analysis of Sentinel-2 images showed that the reflectance of greenhouses changes during crop growth and development. In particular, the red-edge and near-infrared bands undergo a significant increase and then decrease during the whole crop growth period. Thus, two critical period images, containing a substantial difference in greenhouse reflectance, were sufficient to carry out an accurate and efficient mapping result. (2) The 1D-CNN classifier was used to map greenhouses by capturing subtle details and the overall trend of the spectrum curve. Overall, our approach showed higher classification accuracy than other approaches using support vector machines (SVM) or random forests (RF). In addition, the greenhouse area identified was highly consistent with the existing surfaces observed in very high-resolution images, with a kappa co-efficient of 0.81. (3) The narrow band feature differences (red-edge and near infrared narrow bands) in two-temporal Sentinel-2 images played a significant role in high-precision greenhouse mapping. The classification accuracy with narrow band features was much better than the maps produced without narrow band features. This scheme provided a method to digitize greenhouse precisely and publish its statistics for free, which enable advanced decision support for agriculture management. [ABSTRACT FROM AUTHOR]

The effects of meteorological factors on reference evapotranspiration (ET0) are variable on different time scales, although research tends to focus only on certain time scales. Therefore, using the meteorological data from 1958 to 2017 of Beijing, China, ET0 values over the last 60 years were calculated using Penman–Monteith method. The variation in ET0 values was thus analyzed against four meteorological factors over different time scales. The sensitivity of ET0 to these factors was assessed using a sensitivity coefficient, while the contribution of each factor to ET0 change was quantified by combining this sensitivity coefficient with the factor's relative change rate over multiple time scales. The results showed that the sensitivity coefficient of relative humidity over different time scales were all negative, while the sensitivity coefficients of net radiation, temperature and wind speed were mostly positive. The main sensitivity factors of ET0 on different time scales varied. On annual time scales, the main factors were relative humidity and temperature. Over annual time scales, relative humidity and net radiation alternated as the main sensitivity factor; while over interannual time scales, the most sensitive factor was relative humidity during 1958–1979 and net radiation thereafter. The contribution of these four meteorological factors to ET0 also fluctuated greatly on intra-annual time scales. On daily time scales, the contributions of temperature and wind speed at the start and end of the year were large, while net radiation and relative humidity were dominant mid-year. On monthly to seasonal time scales, the contributions of these four meteorological factors to ET0 were notable. The contribution of relative humidity was largest in spring and autumn; net radiation was dominant in summer, while temperature and wind speed were dominant in winter. This research on the temporal variability of ET0 response factors is of great significance for understanding regional climate change. [ABSTRACT FROM AUTHOR]

Due to the large spatial variation of groundwater depth, it is very difficult to determine suitable irrigation schedules for crops in shallow groundwater area. A zoning optimization method of irrigation schedule is proposed here, which can solve the problem of the connection between suitable irrigation schedules and different groundwater depths in shallow groundwater areas. The main results include: (1) Taking the annual mean groundwater depth 2.5 m as the dividing line, the shallow groundwater areas were categorized into two irrigation schedule zones. (2) On the principle of maximizing the yield, the optimized irrigation schedule for spring wheat in each zone was obtained. When the groundwater depth was greater than 2.5 m, two rounds of irrigation were chosen at the tillering–shooting stage and the shooting–heading stage with the irrigation quota at 300 mm. When the groundwater depth was less than 2.5 m, two rounds of irrigation were chosen at the tillering–shooting stage, and one round at the shooting–heading stage, with the irrigation quota at 240 mm. The main water-saving effect of the optimized irrigation schedule is that the yield, the soil water use rate, and the water use productivity increased, while the irrigation amount and the ineffective seepage decreased. [ABSTRACT FROM AUTHOR]

Crop nitrogen monitoring techniques, particularly choosing sensitive monitoring bands and suitable monitoring models, have great significance both in theory and in practice for achieving non-destructive monitoring of nitrogen concentration and accurate management of water and fertilizer in large-scale areas. In this study, a lysimeter experiment was carried out to examine the characteristics of canopy spectral reflectance variation of summer corn under different fertilization levels. The relationship between canopy spectral reflectance and nitrogen concentration was investigated, based on which sensitive bands for the corn canopy nitrogen monitoring were selected and a suitable spectral index model was determined. The results suggest that under different fertilization levels, the canopy spectral reflectance of summer corn decreases with the increase of the canopy nitrogen concentration in the visible light band, but varies in the opposite direction in the near-infrared band, with a premium put on a higher correlation between the spectral reflectance of the characteristic bands and their first derivatives and the canopy nitrogen concentration. The most sensitive bands for monitoring the canopy nitrogen concentration using spectral reflectance and its first derivative are found to be 762 nm and 726 nm and the correlation coefficients are 0.550 and 0.795, respectively. The optimal band combination, generated by multivariate stepwise regression analysis, is composed of 762 nm, 944 nm and 957 nm bands. From the 55 reported spectral index models of crop nitrogen concentration monitoring, the most suitable index model, NDRE, is chosen such that this index model has the highest correlation with the canopy nitrogen concentration in summer corn. This model has a significant positive correlation with the canopy nitrogen concentration at each growth period, and the correlation coefficient is up to 0.738 during the whole growth period. Spectral monitoring models of canopy nitrogen concentration are constructed using sensitive bands, and a combination of bands and the spectral index, suggesting that these models perform well in monitoring. The models arranged in descending order of simulation accuracy are as follows: the suitable spectral index model, the optimal band combination model, the sensitive band reflectance first derivative model, the sensitive band reflectance model. The determination coefficients are 0.754, 0.711, 0.639 and 0.306, respectively. [ABSTRACT FROM AUTHOR]

The adjustment of crop planting structure can change the process of water and material circulation, and thus affect the total amount of water and evapotranspiration in the irrigation district. To guide the allocation of water resources in the region, it is beneficial to ascertain the effects of changing the crop planting structure on water saving and farmland water productivity in the irrigation district. This paper takes Yingke Irrigation District as the background. According to the continuous observation data from 2012 to 2013, Based on the modified Soil and Water Assessment Tool (SWAT) model and taking advantage of monthly scale remote sensing EvapoTranspiration (ET) and crop growth parameters (leaf area index and shoot dry matter), we tested the simulation accuracy of the model, proposed irrigation efficiency calculation methods considering water drainage, and established the scenario analysis method for the spatial distribution of crop planting structure. Finally, we evaluated the changes in water savings in irrigation district projects and resources, the irrigation water productivity and the net income water productivity under different planting structure scenarios. The results indicate that the efficiency of irrigation has increased by 15~20%, while considering drainage, as compared with conventional irrigation efficiency. Additionally, the adjustment of crop planting structure can reduce regional evapotranspiration by 14.9%, reduce the regional irrigation volume by 30%, and increase the net income of each regional water area by 16%. [ABSTRACT FROM AUTHOR]

As a well-built, distributed hydrological model, the Soil and Water Assessment Tool (SWAT) has rarely been evaluated at small spatial and short temporal scales. This study evaluated crop growth (specifically, the leaf area index and shoot dry matter) and daily evapotranspiration at the hydrological response unit (HRU) scale, and SWAT2009 was modified to accurately simulate crop growth processes and major hydrological processes. The parameters of the modified SWAT2009 model were calibrated using data on maize for seed from 5 HRUs and validated using data from 7 HRUs. The results show that daily evapotranspiration, shoot dry matter and leaf area index estimates from the modified SWAT2009 model were satisfactory at the HRU level, and the RMSE values associated with daily evapotranspiration, shoot dry matter, and leaf area index were reduced by 17.0%, 1.6%, and 71.2%, compared with SWAT2009. Thus, the influences of various optimal management practices on the hydrology of agricultural watersheds can be effectively assessed using the modified model. [ABSTRACT FROM AUTHOR]