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【Background and Objective】 The APEX model is a comprehensive watershed-scale model for simulating the effects of management practices on agricultural systems and their impacts on water quality, soil erosion, and nutrient cycling. This paper analyzes the sensitivity of its parameters to water status in soil. 【Method】 The analysis is based on data measured from 2016 to 2019 from an irrigation experiment conducted in Jiaodong in Shandong province. Winter wheat was used as the model plant; the Sobol, Morris, and FAST methods were used to analyze the sensitivities of the APEX model parameters associated with crop growth and water stress. We considered the influences of groundwater depth, rainfall and irrigation. 【Result】 When groundwater depth was 1.25 m, the maximum root depth (RDMX) was the most sensitive parameter affecting evapotranspiration, biomass, and yield, while the maximum potential leaf area index (DMLA) was the most sensitive parameter impacting leaf area index (LAI). When the groundwater depth was increased to 5 m, the sensitive parameters influencing crop evapotranspiration and yield differed, with PARM38 (weight coefficient of water stress calculation) and RWPC1 (proportion of root biomass during germination) becoming the most sensitive parameters. Results calculated from all three methods indicated that as irrigation water increased, the sensitivity of RDMX decreased, while the sensitivities of DMLA, DLAI (peak point in growth season), and WA (potential light energy utilization) increased. The sensitivity of RDMX was significantly higher in dry years than in humid years, as opposed to the sensitivity of DMLA. Uncertainty analysis demonstrated that wheat biomass, yield, and evapotranspiration fell within the 5% to 95% confidence interval of the simulated data. 【Conclusion】 The most sensitive parameters identified by the Sobol, Morris, and FAST methods were consistent, although their sensitivity indexes varied with irrigation treatments, rainfall patterns, and groundwater depth. Considering computational efficiency and accuracy, the Morris method is more suitable for parameter sensitivity analysis of the APEX model. These findings provide valuable insights into the application of the APEX to analyze the impact of environmental conditions on crops.

Categorization of soil organic carbon (SOC) into different functional subpools according to their recalcitrance and protective mechanisms helps better understand ecosystems organic carbon (OC) dynamics, and various attempts have been made to explore the suitable experimental fractionation method for such purpose. However, most previous studies neglected the influences of environmental factors on the effectiveness of varying fractionation methods. Density fractionation has shown great promise in elucidating SOC immobilization mechanisms. Here, we compared three varying types of density fractionation methods (density, density + dispersion, and density + other procedures) for categorizing the SOC into three functional pools, that is, active OC (OCactive), moderately stable OC (OCm‐stable), and stable OC, (OCstable) using global data compiled for 95 sites in 31 published studies, and examined the influences of climate (mean annual temperature [MAT] and annual precipitation), vegetation type, and soil properties (soil depth, clay content, and soil type) on SOC fractions determined by the three density fractionation methods. The percentage of OCm‐stable fraction was found to be highest using the density method and lowest using the density + dispersion method, due to differential density ranges between the two methods. At a global scale, the contents of total SOC and its OC fractions decreased with temperature. Precipitation had no apparent influences on the subdivided SOC fractions using either the density + dispersion method or the method of density + other procedures, whereas soil type constrained the effect of precipitation on SOC fractions using the density method. The percentage of OCm‐stable determined by the density + dispersion method was more responsive to MAT and vegetation type than that by the other two methods. The percentage of OCstable determined by the method of density + other procedures was significantly and positively related to the clay content as the OCstable based on this method included small particles. For all the three methods of fractionation, soil type had a greater influence than the clay content on the SOC fractions, especially the OCm‐stable and the OCstable. For soil type characterized by rich metal oxides, both the density method and the method of density + other procedures could be used for SOC fractionation. For soil type rich in nutrients, the density + dispersion method would have higher sensitivity for distinguishing the OCm‐stable. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Crop yield and water use efficiency (WUE) in a wheat-maize double cropping system are influenced by short and uneven rainfalls in the North China Plain (NCP). A 2-year experiment was conducted to investigate the effects of irrigation on soil water balance, crop yield and WUE to improve irrigation use efficiency in the cropping system. Soil water depletion (ΔSWS) by crop generally decreased with the increase of irrigation and rainfall, while ΔSWS for the whole rotation was relatively stable among these irrigation treatments. High irrigations in wheat season increased initial soil moisture and ΔSWS for subsequent maize especially in the drought season. Initial soil water influenced mainly by the irrigation and rainfall in the previous crop season, is essential to high yield in such cropping systems. Grain yield decreased prior to evapotranspiration (ET) when ET reached about 300 mm for wheat, while maize showed various WUEs with similar seasonal ET. For whole rotation, WUE declined when ET exceeded about 650 mm. These results indicate great potential for improving irrigation use efficiency in such wheat- maize cropping system in the NCP. Based on the present results, reasonable irrigation schedules according to different annual rainfall conditions are presented for such a cropping system. [ABSTRACT FROM AUTHOR]

To help reduce future N loads entering the Gulf of Mexico from the Mississippi River 45%, Iowa set the goal of reducing non-point source N loads 41%. Studies show that implementing winter rye cover crops into agricultural systems reduces N loads from subsurface drainage, but its effectiveness in the Mississippi River Basin under expected climate change is uncertain. We used the field-tested Root Zone Water Quality Model (RZWQM) to estimate drainage N loads, crop yield, and rye growth in central Iowa corn-soybean rotations. RZWQM scenarios included baseline (BL) observed weather (1991–2011) and ambient CO2 with cover crop and no cover crop treatments (BL_CC and BL_NCC). Scenarios also included projected future temperature and precipitation change (2065–2085) from six general circulation models (GCMs) and elevated CO2 with cover crop and no cover crop treatments (CC and NCC). Average annual drainage N loads under NCC, BL_NCC, CC and BL_CC were 63.6, 47.5, 17.0, and 18.9 kg N ha−1. Winter rye cover crop was more effective at reducing drainage N losses under climate change than under baseline conditions (73 and 60% for future and baseline climate), mostly because the projected temperatures and atmospheric CO2 resulted in greater rye growth and crop N uptake. Annual CC drainage N loads were reduced compared with BL_NCC more than the targeted 41% for 18 to 20 years of the 21-year simulation, depending on the GCM. Under projected climate change, average annual simulated crop yield differences between scenarios with and without winter rye were approximately 0.1 Mg ha−1. These results suggest that implementing winter rye cover crop in a corn-soybean rotation effectively addresses the goal of drainage N load reduction under climate change in a northern Mississippi River Basin agricultural system without affecting cash crop production. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (P WP ) 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 P WP 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 P WP 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 P WP 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 P WP of potato across the APE by optimizing planting date and better scheduling the supplemental irrigation from rainwater harvesting. [ABSTRACT FROM AUTHOR]

Climate warming and water resource shortage are severely threatening crop production in Inner Mongolia. Selecting optimal planting date (OPD) is recognized to be an effective way in alleviating water stress by matching crop water requirement with natural precipitation. However, optimal planting dates and drought resistance of staple crops have been not investigated in this region. In this study, the validated APSIM model was used to identify the OPD and crop drought degree for six staple crops including canola, edible sunflower, maize, oats, oil sunflower, and potato through comparing the potential and rainfed yields under different planting dates. The study results showed that OPD varied among crops and regions. Maize had the earliest OPD followed by canola, oil sunflower, oats, potato, and edible sunflower. Optimizing planting date could boost crop yields by increasing precipitation use efficiency. Compared with normal planting date, mean yields and precipitation use efficiencies for staple crops under OPD could be increased by 1%–54% and 7%–119%, respectively in Inner Mongolia. Oats, potato, and canola had higher drought-resistance than other three crops and showed less yield reduction rates and yield variations from potential to rainfed conditions. January–March precipitation (or mean temperature) could be used to determine the OPDs of oats and potato (or edible and oil sunflower), while April precipitation could be used to recommend the OPDs of canola and maize. The study results provide an important reference for selecting optimal planting dates of staple crops and adapting to warming and drying climate for single cropping systems in rainfed regions. [Display omitted] • Optimal planting date (OPD) varied among crops and regions. • Maize, canola, oil sunflower have earlier OPD than oats, potato, edible sunflower. • Oats, potato, and canola have higher drought-resistance than other three crops. • Precipitation and temperature before planting could be used to recommend OPD. [ABSTRACT FROM AUTHOR]

• 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. [ABSTRACT FROM AUTHOR]

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 ( ET a–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. [ABSTRACT FROM AUTHOR]

A shortcoming of the RZ-SHAW model (A hybrid version of Root Zone Water Quality Model and The Simultaneous Heat and Water Model) is that it cannot simulate the plastic mulching technology which is widely used in arid areas. Our objectives in this study were to develop RZ-SHAW to include a new plastic module, and to evaluate the model's performance over three years of maize (Zea mays L.) production in China. A new plastic module was added to compute changes in the shortwave and longwave radiation transfer, turbulent heat and vapor transfer from the surface, and the energy and water balances in the system associated with a plastic mulch layer. The modified RZ-SHAW model can adequately simulate soil water (0.017 cm3 cm−3 ≤ RMSE ≤ 0.030 cm3 cm−3) and capture the evaporation reduction and transpiration increase under plastic mulch. The model overestimated the increased soil temperatures under plastic mulch (2.3 ℃ over the 100-cm profile) compared to the measured data (1.4 ℃). Overall, the revised RZ-SHAW model adequately simulated soil water and heat exchange under plastic mulch conditions. The modified RZ-SHAW model can be used as an effective decision tool for management optimization in plastic mulched cropland. • A new plastic mulch module was developed for the RZ-SHAW model. • The revised model improved soil water and temperature simulation under plastic mulch. • The model captures evaporation reduction and transpiration increase for plastic mulch. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important indicator of the photosynthetic rate and growth status of crops. A few related parameters, such as the red-edge parameters and spectral indices, have been adopted for retrieving the SM of winter wheat. To further study their abilities to detect the SM, field-scale water stress experiments on winter wheat were conducted during the 2018/19 growing season. The spectral ratio index in the near-infrared (NIR) shoulder region (NSRI) (700–1100 nm) was selected by comparing the correlations between the SM and the red edge parameters and spectral indices, and it was optimized using the partial least squares regression (PLSR) method. To assess the performance of the sensitive wavebands of the NSRI in retrieving the SM, three types of spectral index models were established using multiple linear regression (MLR) for the winter wheat from the jointing to the ripening stage. The results indicate that the red-edge parameters are more sensitive to the spectral variation during the jointing and flowering stages. The sensitivity decreased with increasing water stress. The red-edge area (SDr) of winter wheat irrigated in the flowering stage (D1 treatment) and irrigated in the jointing stage (D2 treatment) decreased by 20–30%, respectively. In general, all of the parameters and indices were correlated with the surface SM (0–40 cm depth), especially for the NSRI, with a significant coefficient of determination (R2) of 0.52 in the 10–20 cm depth interval (P < 0.01). Moreover, all of the spectral index models based on the optimized NSRI have good capabilities for retrieving the SM in the jointing stage. The model for one derivative of the logarithm of the NSRI (logarithmic NSRI)' performed best, with R2 and root mean square error (RMSE) values of 0.81–0.92 and 0.17–0.89%, respectively. Finally, the (logarithmic NSRI)' model was used to retrieve the SM in the flowering–ripening stage (R2 =0.85). Overall, the optimized spectral index models can accurately and quickly retrieve the SM and can assist in predicting the effect of drought on the crop yield in the future. [Display omitted] • The responses of red-edge parameters accurately monitor winter wheat growth status under water stress. • The spectral ratio index in the near-infrared shoulder (700–1100 nm) region (NSRI) has the great potential to retrieve soil moisture. • Optimized NSRI spectral index models can improve the accuracy of retrieving soil moisture for winter wheat under water stress. • The accurate soil moisture retrieving results is helpful for predicting the effect of drought on the yields for winter wheat. [ABSTRACT FROM AUTHOR]

Abstract: Currently available models of photosynthate partitioning in crops are poorly developed compared to carbon and water balance models. This paper presents a dynamic photosynthate partitioning model (PPModel) that simulates the partitioning of crop biomass to leaf, stem and root through the interaction between carbon gain (assimilation less respiration) and transpiration, in relation to environmental factors. The central concept is the theory of plant functional equilibrium, in which transpirational loss and water uptake are balanced, within acceptable limits, by a dynamic partitioning of assimilates between shoot and root growth. The model was shown to perform effectively against experimental data for growth and partitioning of biomass in winter wheat (collected over a 2-year period), when environmental factors varied daily and water supply was controlled over a wide range. [Copyright &y& Elsevier]