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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]