Sediment-associated organic carbon and nitrogen inputs from erosion and irrigation to rice fields in a mountainous watershed in Northwest Vietnam

Abstract

Maintaining indigenous nutrient supply and positive nutrient balances are key factors in sustaining rice yields. Irrigation systems act as conveyers for water, sediments and nutrients throughout landscapes, especially in mountainous, cultivated tropical areas where erosivity is usually high. Contributions of erosion and irrigation to the nutrient balance of paddy fields, however, are rarely assessed. In this study, a turbidity-based method was used to quantify sediment-associated organic carbon and nitrogen as well as dissolved nitrogen inputs from erosion and irrigation to a 13 ha rice area in Northwest Vietnam. The irrigation source is a surface reservoir, and both reservoir and irrigation channel are surrounded by permanent upland maize cultivation on the steep slopes. Additionally, organic carbon and nitrogen loads in paddy outflow were determined to obtain nutrient budgets. Irrigation contributed 90 % of sediment-associated organic carbon inputs and virtually all nitrogen inputs. Analysis of ammonium and nitrate in total nitrogen loads showed that 24 % of the total N inputs from irrigation to the rice area, or 0.28 Mg ha−1 a−1, were plant-available. Loads measured at the outlet of rice fields showed that paddies were a trap for sediment-associated nutrients: balancing inputs and outflow, a net load of 1.09 Mg ha−1 a−1 of sediment-associated organic carbon and 0.68 Mg ha−1 a−1 of sediment-associated nitrogen remained in the rice fields. Sediment-associated organic carbon and nitrogen inputs thus form an important contribution to the indigenous nutrient supply of rice in these maize-paddy systems, while the rice fields simultaneously capture nutrients, protecting downstream areas from the effects of land use intensification on surrounding slopes. These results underscore the importance of upland-lowland linkages in tropical, mountainous, erosion-prone areas.

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Acknowledgments

The authors gratefully acknowledge the work of field assistants Do Thi Hoan and Nguyen Duy Nhiem in collecting the dataset, the lab team at the Central Water & Soil Lab at Vietnam National University of Agriculture, under supervision of Associate Prof. Nguyen Huu Thanh, the funding received by the German Research Foundation (DFG) through the SFB Uplands Program, the collaboration with the Center for Agricultural Research and Ecological Studies (CARES) at Vietnam National University of Agriculture, and the three reviewers for their helpful and very constructive comments.

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Correspondence to Johanna I. F. Slaets.

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Appendix: Bootstrap estimates and confidence intervals for sediment loads

Appendix: Bootstrap estimates and confidence intervals for sediment loads

Calculating a measure of uncertainty on this sediment load is not trivial. The final value is a sum of instantaneous loads, and those loads are the product of two predicted values, concentration and discharge, which are not independent of each other, as discharge is a predictor variable for concentration. Additionally, the predicted values are on the transformed scale, and there is serial correlation in the sediment concentration data, as samples are taken closely together in time.

In order to calculate 95% confidence intervals on the sediment loads, a bootstrap method was developed that addresses all of these issues (Slaets et al. in revision). The bootstrap is a Monte Carlo-type method that generates the sampling distribution of a statistic by resampling a large number of times, either from the original observations or from a parametric distribution, to obtain new bootstrap datasets, on each of which the sediment load is calculated. This large number of bootstrap sediment loads provides an empirical distribution, which can be used to estimate the 2.5th and 97.5th percentiles. These percentiles are the limits of the 95% confidence interval (Efron and Tibshirani 1993). In our dataset, 2000 bootstrap replicates resulted in smooth histograms and reproducible percentiles. The developed method thus accounts for uncertainty in the parameter estimates of both the discharge and sediment rating curves, and uncertainty due to residual scatter in the sediment concentrations. In this approach, the final bootstrap process consists of three steps:

  1. (1)

    Non-parametric bootstrapping of the (stage, discharge) pairs in order to obtain 2000 bootstrap stage-discharge equations, and thus 2000 time series predictions for bootstrapped discharge;

  2. (2)

    Non-parametric bootstrapping of the sediment concentration dataset, by drawing whole events (to keep the serial correlation intact) and individual base-flow samples, resulting in 2000 bootstrap sediment rating curves, and thus 2000 time series predictions of continuous suspended sediment concentration;

  3. (3)

    Adding a simulated error term to the concentration predictions to account for inherent residual scatter in the data and to facilitate the back-transformation from the log-scale.

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Slaets, J.I.F., Schmitter, P., Hilger, T. et al. Sediment-associated organic carbon and nitrogen inputs from erosion and irrigation to rice fields in a mountainous watershed in Northwest Vietnam. Biogeochemistry 129, 93–113 (2016). https://doi.org/10.1007/s10533-016-0221-9

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Keywords

  • Nutrient balance
  • Runoff
  • Shifting cultivation
  • Surface reservoir
  • Nutrient yield
  • Water quality