基于辐热积法模拟烤烟叶面积与烟叶干物质产量

Abstract

With respect to planting area and total production,tobacco is one of the world's most important economic crops. Because this crop plant prefers warmth and full sunlight,temperature and solar conditions are widely viewed as the most important factors affecting tobacco quality and yield. Change in leaf area is an important indicator of tobacco growth and yield prediction,and the amount of dry matter accumulating in leaves directly affects yield and indirectly influences quality. In this study,we used a crop growth framework with environmental conditions as driving variables to establish a dynamic mathematical model describing the relationship between temperature,radiation,photosynthetic production,and yield. The two-year study was carried out during 2010 and 2011 in an experimental field using planting conditions optimal for typical tobacco cultivars such as Yuxi and Zhaotong. For the study,we used the tobacco cultivar K326. Based on theoretical photothermal production and experimental data obtained for the effect of temperature and illumination on tobacco leaf growth and dry matter,we established models to explain tobacco leaf area growth and dry matter accumulation applicable to different tobacco-growing areas. We quantified temperature and illumination effects on leaf area growth using the indicators of relative thermal effectiveness and photosynthetically active radiation,and then verified the models by using tobacco leaf area and dry weight simulated data based on temperature and illumination observations from 1989—2011 and independent experimental data. The change in tobacco leaf area by growth period was fitted to a general logistic growth curve using Sigmaplot software after corrections for correlated parameters. Curve fitting for tobacco leaf dry weight data vs. growing degree day( GDD) was performed using SPSS software. Using our model based on thermal effectiveness and photosynthetically active radiation,R2and RMSE between predicted and independent experimental leaf area data according to the 1∶1 straight line were 0. 9634 and 0. 1653 m2per plant,respectively. Corresponding values of 0. 5625 and 2. 1627 m2 per plant were obtained using a specific leaf area( SLA) model,whereas values of 0. 8321 and 0. 9249 m2per plant were calculated for predicted vs. experimental data using a GDD-based model. Compared with analyses of tobacco leaf area carried out using SLA and GDD models,results obtained using the TEP model were more accurate by 93% and 82%, respectively. The RMSE value for leaf dry weight calculated using our model was 27. 1 g / m2. With respect to dry matter accumulation,the degree of fit for simulated and actual observed data was 0. 907 and 0. 982,respectively,with a RE of 24. 5% for the Yuxi tobacco test data over the years of the study. By taking advantage of conventional meteorological observational data,such as that for temperature and sunlight,our model is able to actively predict tobacco leaf area growth and dry matter accumulation. By comprehensively analyzing temperature and light as two key factors affecting crop growth, the model avoids the disadvantages of previous models that inadequately consider temperature and light effects. Our model well explains the crop growth S-curve and is able to relatively accurately predict dry weight during the mature period. This model can consequently provide a theoretical basis for decisions related to tobacco yield prediction,and is thus of great importance for enhancement of economic and ecological benefits of tobacco production in China. Light effects input into this model were based on monthly illumination values,resulting in lowered accuracy of accumulative light effects. Because dry weight data was not adequately used,the analysis of the accumulation process was not fully satisfactory. In addition,it was not possible to accurately simulate effects on dry weight for some years because of the influence of the transplanting period on tobacco growth and dry matter accumulation,leading to large simulation deviations in the medium-term growth period. As a consequence,more test data are needed to enhance accuracy and general applicability of our model.