Dynamic estimation of summer maize biomass based on parameter adjustment of crop growth model

Biomass is one of the important growth indicators of summer maize (Zea mays). Timely and accurate acquisition of field summer maize biomass data and its dynamic changes is conducive to county agricultural management departments to rationally adjust field production management measures, which is of great significance for increasing maize yield based on the establishment of a simulation model of summer maize biomass process.In this paper, we analyzed above ground biomass and its variation characteristics of summer maize at different growth stages and discussed the feasibility of adjusting parameters of biomass simulation modelusing measured leaf area index and biomass datain Dafengdistrict ofYanchengcity, Jiangsu province, along the east coast of China. The simulation model of biomass process of summer maize could estimate the dynamic changes of aboveground biomass of summer maize at different growth stages from emergence to grain filling.Firstly, the simulation model can predict above ground biomass effectively because of biomass accumulation mainly coming from leaf formation from seedling emergence to pre-jointing growth stage, the root mean square difference (RMSE) of which was 18.31 kg/hm2. During the growth stage from jointing to tasseling stage, the biomass accumulation accelerate due to the elongation of stem nodes and the increase of number of nodes, and the difference between the predicted and measured values waslarge and RMSE was 825.94 kg/hm2. For example, the predicted value of biomass at early jointing stage was 535.5 kg/hm2, and the measured value was 480 kg/hm2, with a difference of 11.56%. Another example, the predicted value of biomass beforetasseling stage was 7036.46 kg/hm2, and the measured value was 5 794 kg/hm2, the difference was 21.44%. Then, adjusting the parameters of the model, the predicted biomasswas 6 036 kg/hm2, which was close to the measured biomasswith RMSE 219.43 kg/hm2. Finally, by using the simulation model adjusted by the parameters, we predictedbiomass during the growth period from tasseling to grain filling that predicted values were in good agreement with the measured values, and the determinant coefficient between them was 0.978 and RMSE was 182.95 kg/hm2. For example, the predicted biomass at grain filling stage before parameter adjustment was12 492 kg/hm2, the measured biomass was 10 785 kg/hm2, the relative error was15.83%, and the predicted biomass after parameter adjustment was 11 156 kg/hm2with the relative error of 3.44%.This study provided an effective informatics method for forecasting the aboveground biomass and its dynamic changes of summer maize at different growth stages, and could assist the county agricultural management departments to adjust production measurestimely. Combining remote sensing data with crop process simulation model to estimate regional crop biomass or yield was a hot topic in the remote sensing application in agriculture. In this paper, crop process simulation model was used to analyze the trend of biomass change at three growth stages of summer maize, namely, emergence to jointing, jointing to tasseling, tasseling to filling, and to clarify the law of biomass accumulation and nutrient uptake characteristics at corresponding growth stages. In the follow-up study, LAI and biomass inversion using remote sensing data will be further considered, and assimilation and application of remote sensing data and crop process simulation model will be studied to enhance the universality and effectiveness of maize biomass process simulation model in maize planting areas along the East coast in China.