| Excessive nitrogen inputs in intensive vegetable production not only easily lead to degradation of vegetable quality and waste of resources but also increase the risk of nitrate leaching to groundwater through very high accumulation in soil profile and these threaten human health and the environment. It is imperative to lower excessive N inputs and utilize multiple-objective N management in which both vegetable production and environmental protection are optimized. Based on the N absorption patterns of different vegetable crops, crop growth processes can be simulated with a modeling approach in order to develop N management system with multiple-objectives for intensive vegetable production. Taking into account of N_min residue in root zone at preplanting and N contribution from soil organic pool and environmental resources during the growth period, the structure and related database of N management system was developed and validated in the field experiements in Tongzhou, Beijing, with the optimization of model parameters.Using Richards functions vegetable growth processes could be simulated to predict N absorbing patterns despite the high variation in growth and N absorption characteristics of different vegetable species existed. Good correlations between predicted and measured values in N uptake for tomato, pepper, cauliflower, lettuce and radish were investigated in the field experiments. Multiseasonal cultivation is important for vegetable production, therefore seasonal variation became a key issue that needs addressing in simulating and predicting vegetable production. The model introduced a seasonally adjusted parameter to revise the Richards function. By using the revised parameter, the model can predict biomass accumulation in different growing seasons as observed in field experiments on spinach. In spinach simulations, autumn was set as the standard growth season with the seasonal parameter as 1.0. By adjusting the expected target yield and seasonal parameters, the model satisfactorily predicted spring yields despite the large differences in yield between spring and autumn spinach. A series of field experiments showed that significant correlation between predicted and measured values in vegetable dry matter or N uptake by using the modified Richards function under different growth conditions and management practices for different vegetable species, thus demonstrating its feasibility in simulating yield and N absorption for vegetable production.N_min Buffer refers to the amount of the necessary N_min in soil for vegetable growth, the value of which is determined by innate characteristics of vegetable species such as N absorption rate, absorption capacity and architecture of root systems. Results from different N applications show that N_min Buffer of different vegetables differed greatly. N_min Buffer was 41, 55 or 62 kg ha-1 N for cabbage, cauliflower and carrot, respectively. Between-region variation for autumn spinach N_min Buffer was small when experiments conducted in Laiyang, Shandong Province and Beijing suburb were compared and between-year variation for autumn spinach was small in rural Beijing. However, the inter-season variations were high with regard to spring and autumn spinach cultivation as observed in Laiyang withan Nmin Buffer requirement of 56 kg ha-1 N for spring spinach, a value one-third higher than that for autumn spinach.Nitrogen mineralization from soil organic matter is one of the important N sources for vegetable growth. Generally speaking, there are numerous factors influencing N mineralization, resulting in wide variation under different conditions and values ranging from -440 to 193 kg ha-1. In rural Beijing, the average level was 1.34 kg ha-1 day-1 N.Irrigation is a key factor influencing soil N transformations and management of intensive vegetable production. Although furrow irrigation consumed water over 30% more than sprinkler irrigation for spinach, the yields and N absorption were still lower than under sprinkler irrigation with the same N application rate. The value of the N_m... |
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