There is a growing concern about excessive nitrogen (N) and water use in agricultural systems in North China due to the reduced resource use efficiency and increased groundwater pollution. A two-year ...experiment with two soil moisture by four N treatments was conducted to investigate the effects of N application rates and soil moisture on soil N dynamics, crop yield, N uptake and use efficiency in an intensive wheat–maize double cropping system (wheat–maize rotation) in the North China Plain. Under the experimental conditions, crop yield of both wheat and maize did not increase significantly at N rates above 200 kg N ha-1. Nitrogen application rates affected little on ammonium-N (NH4-N) content in the 0–100 cm soil profiles. Excess nitrate-N (NO3-N), ranging from 221 kg N ha-1 to 620 kg N ha-1, accumulated in the 0–100 cm soil profile at the end of second rotation in the treatments with N rates of 200 kg N ha-1 and 300 kg N ha-1. In general, maize crop has higher N use efficiency than wheat crop. Higher NO3-N leaching occurred in maize season than in wheat season due to more water leakage caused by the concentrated summer rainfall. The results of this study indicate that the optimum N rate may be much lower than that used in many areas in the North China Plain given the high level of N already in the soil, and there is great potential for reducing N inputs to increase N use efficiency and to mitigate N leaching into the groundwater. Avoiding excess water leakage through controlled irrigation and matching N application to crop N demand is the key to reduce NO3-N leaching and maintain crop yield. Such management requires knowledge of crop water and N demand and soil N dynamics as they change with variable climate temporally and spatially. Simulation modeling can capture those interactions and is considered as a powerful tool to assist in the future optimization of N and irrigation managements.
The cropping system conversion, from rice to vegetable, showed various influences on the greenhouse gases (GHG) emission with conversion time and fertilizer/irrigation management. In this study, we ...evaluated the DeNitrification-DeComposition (DNDC) model for predicting carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) emissions and crop yields as rice converted to vegetable cropping system under conventional or no fertilization from 2012 to 2014. Then, we quantified the long-term (40 years) impacts of rice-vegetable cropping system conversions and fertilization levels (0, 50, 100 and 150% conventional fertilization rate) on GHGs emissions and global warming potentials (GWP) using the calibrated model. The DNDC model-simulated daily GHG emission dynamics were generally consistent with the measured data and showed good predictions of the seasonal CH4 emissions (coefficient of determination (R2) = 0.96), CO2 emissions (R2 = 0.75), N2O emissions (R2 = 0.75) and crop yields (R2 = 0.89) in response to the different cropping systems and fertilization levels across the two years. The overall model performance was better for rice than for vegetable cropping systems. Both simulated and measured two-year data showed higher CH4 and CO2 emissions and lower N2O emissions for rice than for vegetable cropping systems and showed positive responses of the CO2 and N2O emissions to fertilizations. The lowest GWP for vegetable without fertilization and highest the GWP for rice with fertilization were obtained. These results were consistent with the long-term simulation results. In contrast to the two-year experimental data, the simulated long-term CH4 emissions increased with fertilization for the rice-dominant cropping systems. The reasonable cropping systems and fertilization levels were recommended for the region.
•CWPF are prerequisites for limited water irrigation.•The CWPF must be based on long-term field data to account for variation in climate.•We modeled location-specific long-term averaged CWPF for corn ...(Zea mays L.).•We used a system model, short-term field experiments and long-term weather data.•CWPF are transferable across locations.
Crop water production functions (CWPFs) are often expressed as crop yield vs. consumptive water use or irrigation water applied. CWPFs are helpful for optimizing management of limited water resources, but are site-specific and vary from year to year, especially when yield is expressed as a function of irrigation water applied. Designing limited irrigation practices requires deriving CWPFs from long-term field data to account for variation in precipitation and other climatic variables at a location. However, long-term field experimental data are seldom available. We developed location-specific (soil and climate) long-term averaged CWPFs for corn (Zea mays L.) using the Root Zone Water Quality Model (RZWQM2) and 20 years (1992–2011) of historical weather data from three counties of Colorado. Mean CWPFs as functions of crop evapotranspiration (ET), ET due to irrigation (ETa–d), irrigation (I), and plant water supply (PWS=effective rainfall+plant available water in the soil profile at planting+applied irrigation) were developed for three soil types at each location. Normalization of the developed CWPF across soils and climates was also developed. A Cobb–Douglas type response function was used to explain the mean yield responses to applied irrigations and extend the CWPFs for drip, sprinkler and surface irrigation methods, respectively, assuming irrigation application efficiencies of 95, 85 and 55%, respectively. The CWPFs developed for corn, and other crops, are being used in an optimizer program for decision support in limited irrigation water management in Colorado.
An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize ...experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals.
•Soil microbial biomass was not affected by 10-yr of N fertilization or plastic mulch.•N fertilization and mulch differentially impacted soil bacteria and fungi.•Mulch increased the N threshold that ...caused changes in microbial community structure.•Soil moisture and N content were main drivers of microbial community structure.•N fertilization enhanced but mulch reduced the stability of the microbial network.
Soil microbes are crucial for improving soil quality and productivity. Plastic film mulch (FM), in conjunction with fertilization, has significantly improved crop yields over vast areas of dryland production. However, how these practices affect soil microbial communities, especially as regards co-occurrence patterns within microbial taxa, is unclear. The objective of this study was to determine the effects of 10 years of FM and four nitrogen (N) fertilization rates 0 (N0), 100 (N100), 250 (N250), and 400 (N400) kg N ha−1 on soil bacterial and fungal diversity, community structure, composition, and the co-occurrence network in a rainfed maize (Zea mays L.) field on the Loess Plateau of China. Results showed that N fertilization and FM did not affect soil microbial biomass carbon, but these practices changed the soil bacterial and fungal community structures. The bacterial community structure was dominantly affected by N fertilization, owing to the increased soil N content and decreased soil pH, which reduced bacterial community diversity and altered the relative abundance of some copiotrophic/oligotrophic taxa (e.g., Gemmatimonadetes, Acidobacteria, Rokubacteria, and Planctomycetes). Plastic mulch played a greater role in regulating the fungal community structure, primarily because FM increased soil moisture and promoted soil organic matter decomposition, thereby reducing fungal richness and altered the relative abundance of Chytridiomycota, Mortierellomycota, Glomeromycota, and Mucoromycota. Moreover, FM mediated the effects of N fertilization by reducing soil N content, and then increased the N threshold that caused changes in microbial structure. Network analysis indicated that FM caused an unstable co-occurrence network with fewer positive and negative links, while N fertilization increased both positive and negative (except N400) links, indicating enhanced cooperation and competition among microbes. These results indicate that long-term plastic mulch and high N fertilization could result in risk for soil quality in terms of soil microbial community structure and stability, suggesting that developing new management strategies is necessary to sustain dryland productivity.
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•Yield and water-nitrogen use efficiency under three levels were determined by APSIM.•To achieve potential yield, irrigation needs to be increased rather than nitrogen.•WUE and NPFP ...could be increased significantly by reducing water/nitrogen inputs.•This implies a high potential for wheat yield and water-nitrogen efficiency win-win.
Increasing both grain yield and water use efficiency (WUE)/nitrogen partial factor productivity (NPFP) of winter wheat is crucial to realize the sustainable development of agricultural production in the North China Plain (NCP). This study was conducted to test the hypothesis that a trade-off between yield and efficiency could reduce water and nitrogen inputs of winter wheat in the NCP. Wheat yield, WUE and NPFP under three production levels, i.e. the potential, high-yield and high-efficiency (HH), and on-farm, and their gaps were investigated with APSIM-Wheat model. The results showed that simulated potential yields were close to observed potential yields with RMSE of 1150 kg ha−1 (NRMSE of 12 %) and simulated on-farm yields followed with observed yields with RMSE of 576 kg ha−1 (NRMSE of 8.8 %). Simulated yield gap between the potential and on-farm yields was 2565 kg ha−1 averaged across the NCP from 1981 to 2015 with the highest yield gap in the central part of NCP and the eastern Shandong province, and the corresponding gaps of WUE and NPFP were 0.45 kg m−3 and 10.9 kg N kg−1 with a large spatial difference. To narrow the gaps, about 33 mm additional irrigation and 5 kg N ha−1 reductions from the current irrigation (242 mm) and N fertilizer (267 kg N ha-1) application amounts were needed across the NCP. WUE and NPFP could be increased by 29 % and 43 % from the on-farm to the potential levels. However, if on-farm yield only attained 80 % of the potential, WUE and NPFP could be increased by 0.96 kg m−3 (60 %) and 19.3 kg kg−1 (77 %) across the NCP. Irrigation and nitrogen fertilization amounts could be reduced by averaged 127 mm and 89 kg ha−1 from current averaged irrigation and nitrogen fertilization amounts across the NCP. Especially, the irrigation schedule at on-farm level should be adjusted from three or four irrigations at (sowing), overwintering, jointing and flowering to two irrigations at jointing and flowering across the NCP. Our results suggested an explicit potential for wheat yield and water-nitrogen efficiency win-win by optimizing water and nitrogen management in the NCP.
•Optimal planting date was postponed from east to west with delaying rainy season.•Supplemental irrigation at the tuberization stage produced higher potato yield.•Optimal planting date varied with ...supplemental irrigation schedule.
Adjusting planting date along with supplemental irrigation is widely used to improve potato yield in the agro-pastoral ecotone (APE) with high variability of limited rainfall in North China. Optimal planting date and supplemental irrigation time for potato differed greatly with climate and soil conditions, and were not fully investigated via field experiments. In this study, using APSIM-Potato model carefully calibrated and validated with two-years serial planting experimental data, the individual and coupled impacts of planting date and supplemental irrigation time on yield and water productivity (PWP) of potato were quantified across the APE. APSIM-Potato performed well in simulating phenology, leaf area index (LAI), soil water dynamics, biomass of potato, and also captured the trend in potato yields under different planting dates. Based on the long-term simulations from 1981 to 2010, the optimal planting dates were May 10 (local normal planting date), May 20 and May 30 in the eastern, middle and western APE, respectively. Yield and PWP of potato could be increased by 12.5% and 7.0% in the middle APE, 23.3% and 18.3% in the western APE respectively, under the optimal planting date compared with the local normal planting date under rainfed condition. Supplemental irrigation (8-55 mm) from rainwater harvesting could increase potato yield by 3.5-35.2%, 6.9-41.8%, and 9.0-50.8% respectively, in the eastern, middle and western APE. The corresponding PWP could be enhanced by 1.2-22.7%, 6.7-30.8% and 4.5-33.7%, respectively. Combining the optimal planting date with better scheduling the maximal harvested rainwater could increase yield and PWP of potato by 36.8% and 23.4%, 69.2% and 49.2%, 64.3% and 48.8%, respectively for the eastern, middle and western APE, compared with the simulation results under the local normal planting dates and rainfed condition. The study suggested a large potential of increasing yield and PWP of potato across the APE by optimizing planting date and better scheduling the supplemental irrigation from rainwater harvesting.
Potato is a staple food crop in the agro-pastoral ecotone (APE) of North China. However, the potato yield is low and highly variable due to limited water and nutrient availabilities in the region. ...Irrigation and nitrogen (N) fertilization have been used widely to enhance potato yield but result in negative environmental impacts in the APE. This study aims to explore the optimum combinations of irrigation and N fertilization for different potato production goals by using APSIM-Potato model calibrated well by field experiments with different combinations of irrigation and N fertilizer conducted at the typical site in the APE. Long-term (1981–2010) simulation for potato yield, water use efficiency (WUE), nitrogen use efficiency (NUE), economic profits and environmental impacts were analyzed under different combinations of irrigation (IR, based on the soil water deficit, ranged from 10 (IR10) to 100 mm (IR100) with the interval of 10 mm) and N fertilization (ranged from 0 (N0) to 210 (N210) kg ha−1 with the interval of 30 kg ha−1). Combination of IR10 and N210 maximized potato yields in the whole APE, and the yield was highest in the middle APE, with the value of 35.2 t ha−1, which was 6.7% and 2.1% higher than that in the eastern and western APE. However, such water and nitrogen managements would cause annual decline of groundwater table by 1.6 m and N loss by 10.9 kg ha−1. In order to achieve the highest WUE, the irrigation amounts should be 124, 107 and 132 mm in the eastern, middle and western APE, respectively, coupled with 90 kg ha−1 N fertilizer, and the highest WUEs were 89.6 kg ha−1 mm−1, 93.1 kg ha−1 mm−1 and 84.8 kg ha−1 mm−1 in the eastern, middle and western APE. For highest FNUE, the combination should be IR10 and N30 across APE, and the highest values were 959 kg ha−1 kg−1, 1092 kg ha−1 kg−1 and 1022 kg ha−1 kg−1 in the eastern, middle and western APE. Moreover, to get the highest income, the irrigation ranged from IR50 to IR10 and the amounts of N fertilizers ranged from 30 kg ha−1 to 120 kg ha−1, and the maximum incomes were 18,250 CNY ha−1, 20,060 CNY ha−1 and 19,660 CNY ha−1 in the eastern, middle and western APE. In all, the combination that maximized the income could contain the relative higher yield, WUE, NUE and lower environmental sequence. Our study would be helpful in determining the optimal amounts of irrigation and N fertilization for different goals of potato production in the APE.
•Optimal irrigation and N for different potato production goals was recommended.•Irrigation applied if soil water deficit ≥10 mm and 210 kg ha−1 N maximized yield.•Requirement of irrigation and N for maximum WUE were different in the APE.•Irrigation applied if soil water deficit ≥10 mm and 20 kg ha−1 N maximized NUE.•Combinations of irrigation and N that maximized income varied across the APE.
•RF-based ETp models generally outperformed empirical ETp models in southeastern Australia.•ETp would increase under future climate scenarios independent of ETp models and stations.•Increases of Tmax ...and Rs were the main reasons leading to ETp increase.•The differences between RCPs contributed the most to the uncertainty in ETp projections.
Projecting the likely change of potential evapotranspiration (ETp) under future climate scenarios is crucial for quantifying the impacts of climate change on the hydrologic cycle and aridity conditions. However, there are different sources of uncertainty in projecting future ETp that may arise from global climate models (GCMs), emission scenarios, and multiple ETp models used. In this study, we developed three random forest-based (RF-based) ETp models with solar radiation and air temperature at eight climatic stations in southeastern Australia. With Penman model as the benchmark, their performance was firstly compared with four empirical models (Jensen-Haise, Makkink, Abtew, and Hargreaves), which requires the same meteorological inputs. In general, the RF-based ETp models showed better performance in ETp estimates across all stations, with coefficients of determination (R2) ranging from 0.68 to 0.92, root mean square errors (RMSE) ranging from 0.58 mm day−1 to 1.46 mm day−1, and relative mean bias errors (rMBE) ranging from −16.10% to 9.73%. The RF-based and empirical models were then used to project future ETp for the eight stations based on statistically downscaled daily climatic data from 34 GCMs under two different representative concentration pathways (RCP4.5 and RCP8.5). All models indicated that ETp was likely to increase at the eight stations. The ensemble increases of mean ETp across eight stations ranged from 33 mm year−1 (2.1%, 2040s) to 129 mm year−1 (9.2%, 2090s) and from 43 mm year−1 (2.8%, 2040s) to 248 mm year−1 (17.6%, 2090s) under RCP4.5 and under RCP8.5, respectively. In addition, we also quantified uncertainties in ETp projections originating from ETp models, GCMs, RCPs, and their combined effects using the analysis of variance (ANOVA) method. Results showed that RCP-related uncertainty contributed the most to projected ETp uncertainty (around 40% for most stations) while GCM-related and ETp model-related uncertainties accounted for roughly equal amounts of projected ETp uncertainty (10%–30%). This study demonstrated the better performance of RF-based ETp models. It is advisable to use multiple ETp models driven by various GCMs under different RCPs to produce reliable projections of future ETp.
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