In order to optimize N management in organic farming systems, knowledge of crop growth processes in relation to N limitation is necessary. The present paper examines the response of potato (Solanum tuberosum L.) and wheat (Triticum aestivum L.) to N with respect to intercepted photosynthetically active radiation (PAR), light use efficiency (LUE), and leaf N concentration ([N]). Potato and wheat cultivars were grown in field experiments (1997 and 1998) at three N levels: no N (N1), cattle (Bos taurus) slurry (N2), and cattle slurry supplemented by mineral N fertilizers (N3). Estimated available N from the soil (0–0.9 m) plus added fertilizer was 80 (N1), 150 (N2), and 320 (N3) kg ha−1 for potato and 115 (N1), 160 (N2), and 230 (N3) kg ha−1 for wheat. Nitrogen deficiency was quantified by an N nutrition index (NNI; 1 = hardly limited, 0 = severely limited). Nitrogen deficiency increased in the N1 and N2 treatments up to 20 (potato) and 50 (wheat) d after emergence, with small changes thereafter. An increasing N limitation in potato (NNI = 1–0.55) resulted in a linear decrease in crop dry weight and cumulative intercepted PAR and in a linear increase of the harvest index, whereas the LUE decreased only at NNI values below 0.65. Crop dry weight and cumulative intercepted PAR for wheat decreased linearly with N limitation (NNI = 0.9–0.6), but the harvest index and LUE were unaffected. For both crops, N limitation to 0.55 caused a linear decrease in maximum leaf area index, the rate of foliar expansion, leaf area duration, and to a lesser extent, leaf [N]. In conclusion, both crops respond to N limitation by reducing light interception while maximizing the LUE and leaf [N].

Publications in official statistics are increasingly based on a combination of sources. Although combining data sources may result in nearly complete coverage of the target population, the outcomes are not error free. Estimating the effect of nonsampling errors on the accuracy of mixed-source statistics is crucial for decision making, but it is not straightforward. Here we simulate the effect of classification errors on the accuracy of turnover-level estimates in cartrade industries. We combine an audit sample, the dynamics in the business register, and expert knowledge to estimate a transition matrix of classification-error probabilities. Bias and variance of the turnover estimates caused by classification errors are estimated by a bootstrap resampling approach. In addition, we study the extent to which manual selective editing at micro level can improve the accuracy. Our analyses reveal which industries do not meet preset quality criteria. Surprisingly, more selective editing can result in less accurate estimates for specific industries, and a fixed allocation of editing effort over industries is more effective than an allocation in proportion with the accuracy and population size of each industry. We discuss how to develop a practical method that can be implemented in production to estimate the accuracy of register-based estimates.

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