Nitrogen dynamics in flooded soil systems: an overview on concepts and performance of models

Reviewing the role of biochar in paddy soils: An agricultural and environmental perspective

The main challenge of the twenty-first century is to find a balance between environmental sustainability and crop productivity in a world with a rapidly growing population. Soil health is the backbone of a resilient environment and stable food production systems. In recent years, the use of biochar to bind nutrients, sorption of pollutants, and increase crop productivity has gained popularity. This article reviews key recent studies on the environmental impacts of biochar and the benefits of its unique physicochemical features in paddy soils. This review provides critical information on the role of biochar properties on environmental pollutants, carbon and nitrogen cycling, plant growth regulation, and microbial activities. Biochar improves the soil properties of paddy soils through increasing microbial activities and nutrient availability, accelerating carbon and nitrogen cycle, and reducing the availability of heavy metals and micropollutants. For example, a study showed that the application of a maximum of 40 t ha-1 of biochar from rice husks prior to cultivation (at high temperature and slow pyrolysis) increases nutrient utilization and rice grain yield by 40%. Biochar can be used to minimize the use of chemical fertilizers to ensure sustainable food production.

Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R 2 = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R 2 < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R 2 of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.