Study On Nitrogen Uptake Model Based On Simulation Of Development And Growth For Drip Irrigated Processing Tomatoes

Crop growth simulation model is the use of systems theory approach to quantitatively describe and predict crop growth and development process and its dynamic relationship with the environment. It has important significance on optimizing the growth and development environment and managing cultivation measures of plants. Nitrogen, which is the greatest demand for agricultural production, plays important roles on improving crop yields and promoting the quality of agricultural products. Drip irrigation is considered as a brand-new water-saving technology in arid and semi-arid areas, being many advantages, such as saving water and fertilizer, increasing crop yield, and improving quality. Processing tomato (Lycopersicon esculentum Mill) is a special advantage industry in Xinjiang province (arid region). Therefore, processing tomato industry occupies an important position in planning and development of agriculture in Xinjiang. In recent years, processing tomato industry develops quickly and becomes one of the three greatest industries in agriculture. Both yield production of processing tomato and export volume of tomato sauce rank first in the country. So, the core problemes in industrial development of processing tomato mainly concentrated into intensive development, modern management and cultivation. Excessive application of nitrogen fertilizer can not only increase production but also resulte in waste nitrogen, ecological adversely in the course of processing tomato planting. Therefore, precise application of nitrogen fertilizer is particularly important for improving the processing tomato production in drip irrigation, reduce cost of agricultural production.This study was conducted from2010to2012in Shihezi university of Agriculture Experiment Station with different sowing date and nitrogen level in different test varieties. Dry matter accumulation model, dry matter distribution model, and yield formation model were constructed using sowing dates based on physiological development time (PDT). Also, we investigated the effect of nitrogen levels on aboveground biomass and the dynamic change of nitrogen accumulation and nitrogen utilization efficiency of processing tomato in drip irrigation. Meanwhile, the critical nitrogen concentrate dilution curve of the whole plant and leaf blades, the critical nitrogen concentrations absorption model, and nitrogen nutrition index model of processing tomato in drip irrigation were then constructed. Finally, appropriate nitrogen application scope was calculated for processing tomato in drip irrigation based on plant nitrogen nutrition diagnosis. The intentions of this study are to integrate management of water and fertilizer and effectively control the growth environment for accurate and precise application of nitrogen fertilizer to provide the theoretical basis and technical support. The main contents and results of this study were as follows:1. The leaf area index(LAI)and specific leaf area(SLA)simulation model of processing tomato with drip irrigated were developed based on the accumulated physiological development time after emergence (PDT). Then a simulation of leaf area, dry matter production and accumulation of processing tomato with drip irrigated was developed by integrating the based on physiological and ecological processes of photosynthesis and dry matter production simulation model. The results show that:When using the model based on PDT, the coefficient of determination (R2), root mean squared error (RMSE) and modelling efficiency indexes (ME) between simulated and measured leaf area index (LAI) based on the1:1line were0.9265,12.87%and0.9724, respectively. However, when using the model based on SLA, the R2, RMSE and ME between simulated and measured LAI based on the1:1line were0.6758,42.24%, and0.7124, respectively. When using the model based on PDT, the R2, RMSE and ME between simulated and measured aboveground dry matter weight based on the1:1line were0.9903,11.91%and0.9901, respectively. However, when using the model based on SLA, the R2, RMSE and ME between simulated and measured aboveground dry matter weight based on the1:1line were0.8956,31.29%and0.7504, respectively. Compared with the SLA method, PDT method to improve the processing tomato leaf area index prediction accuracy while also improving the prediction accuracy of the aboveground dry matter weight.2. Relationship between partitioning indexes of organ dry matter and physiological development time (PDT) were systematically studied with the experiment of different sowing dates and varieties. And simulation models for shoot dry matter partitioning and yield in drip irrigated processing tomato was developed based on partitioning index (PI) and harvest index (HI). In which PI of leaf and HI were the functions of PDT, which were also altered by relative thermal effectiveness (RTE), relative photoperiod effectiveness (RPE) and intrinsic development factor (IDF). Model validation with three years weather and independent crop growth data showed that the growth and yield of processing tomato are simulated satisfactorily. R2, root mean square error (RMSE) and relative estimation error (RE) of simulated and observed dry matter under four different growing stages (emergence to flowering, flowering to fruit-setting, fruit-setting to maturing, maturing to ending date), total dry weight of whole growth period, stem dry weight, leaf dry weight, fruit dry weight were0.9754,0.029t/hm2,11.43%;0.9936,0.074t/hm2,5.09%;0.9840,0.250t/hm2,6.83%;0.9713,0.102t/hm2,5.71%;0.9940,0.504t/hm2,8.06%;0.9629,0.332t/hm2,14.62%;0.9828,0.200t/hm2,10.84%;0.9585,0.549t/hm2,18.30%, the R2、RMSE and RE between the predicted and the measured yield based on the1:1line were0.9658,5.806t/hm2,8.07%, respectively. Which indicated the model could well predict the dynamic accumulation of dry matter in different organs under diverse conditions of drip irrigated processing tomato.3. Three field experiments were conducted to simulate the dynamics changes of different N fertilization rateson above-ground biomass, N accumulation and utilization of drip-irrigated processing tomatoes. The results showed that logistic models best described the changes in above-ground biomass, N accumulation, and utilization of accumulated N efficiency with physiological development time (PDT). Rapid accumulation of N began about4-6d (PDT) earlier than rapid accumulation of above-ground biomass. The momentary utilization rate of N (NMUR) increased after emergence, reached a single peak, and then decreased. In different N application rates, the300kg/hm2treatment caused the largest increases in above-ground biomass and total N accumulation among the N treatments in this study. The eigenvalues for the dynamics of N accumulation and above-ground biomass were highest for the300kg/hm2treatment. The Quadratic model indicates that application rates of349to382kg/hm2is optimum for drip-irrigated processing tomatoes in northern Xinjiang.4. In many crops nitrogen (N) concentration decreases with increasing plant mass. A critical N concentration in plant aboveground biomass, which is defined as the minimum N concentration required for maximum plant growth, can be found at any time in the growth cycle. To determine the critical N concentration dilution curve for drip irrigated processing tomato, three years of field experiment with five levels of N applications were carried out in Shihezi city, northern Xinjiang. Results showed that N concentration in aboveground biomass declined with accumulated physiological development time after emergence. Relationship between the aboveground biomass and critical N concentration can be described by power equation (Nc=4.352DW-0274), with ac=4.352,b=0.274for three experiments. Taking into account all data from the three experiments, we observed a large variability of total-N concentration for a given biomass. Using observed the maximum, Nmax and the minimum N concentration,%Nmin at each sampling date, the following two boundary curves were determined. The boundary curve model also follow a power equation (Nmax=5.063DW-0.246, Nmin=3.522DW0.163), with amax=5.063,bmax=0.246, and αmin=3.522,bmin=0.163for three experiments, respectively. Based on the critical N concentration dilution model, the model of allometric relationships between crop N uptake at each N application level and aboveground biomass, and the model of nitrogen nutrition index (NNI) were developed. The former can be used as an index for controlling of N application and the latter can be used to express the N status of the drip irrigated processing tomato plants. If NNI=1, N nutrition is considered to be optimum, NNI>1indicates excess N. NNI<1indicates N deficiency. Based on the critical N concentration model, the model of N uptake at growth period for potential growth and yield was developed. According to the N uptake model coefficient, NNI and N uptake under critical N concentration, we concluded that300kg/hm2could be used as the optimum N application rate of drip irrigated processing tomato in northern Xinjiang.5. Critical leaf N concenctration (LNc) can be used to predict the N status of processing tomato plants and the NNI (N nutrition index) can quantify the intensity of both N deficiency and luxury consumption. However, no experiments have used leaf N concentrations to develop critical N dilution curves for processing tomatoes. Field experiments were conducted during three consecutive years at Shihezi, Xinjiang, China. The LNc dilution curve for processing tomato, based on leaf N concentration, was described by the equation (LNc=4.408DW-0.605). We used the LNc at each sampling data to calculate the LNNI. The results indicted that the optimum application rate for N fertilizer was300kg/hm2. The LNc has useful applications for crop modelling, for monitoring crop N status, and for making decisions about N fertilization.