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Soil-based irrigation and the partial substitution of chemical fertilizers with manure are promising practices to improve water and nitrogen (N) use efficiency. We hypothesize that their combination would simultaneously benefit potato production, tuber quality and profitability. A two-year experiment was conducted in semiarid northern China to investigate the combined effects of three water treatments [rainfed (W0), soil-based irrigation (W1), conventional irrigation (W2)] and three N treatments [no N (N0), chemical N (N1), 25% manure substitution (N2)] on these indicators, and to perform a comprehensive evaluation and correlation analysis. The results showed that water and N treatments separately affected all indicators except vitamin C content. Compared to W2, W1 significantly increased water productivity by 12% and irrigation water use efficiency (IWUE) by 30% due to 10% lower evapotranspiration and 21% lower water use. However, W1 and W2 negatively affected crude protein content. Conversely, this was compensated by the combination with N1 and N2. There were slight differences between N1 and N2 for all indicators on average across water treatments, while under W1, N2 significantly increased leaf area index (LAI) and N recovery efficiency (REN) by 18% and 29.4%, respectively, over N1. Also, comprehensive evaluations showed that W1N2 performed best, with the highest tuber yield, profit and acceptable quality. This can be explained by the increase in LAI, IWUE and REN due to the positive correlations with tuber yield and net return. Consequently, soil-based irrigation combined with 25% manure substitution had complementary effects on tuber quality and synergistic effects on potato productivity and profitability. [ABSTRACT FROM AUTHOR]

Effective discriminant analysis is of great practical importance, as demonstrated by economic and genetic applications. Feature augmentation via nonparametric and selection (FANS) is an efficient approach that has been widely used in classification. However, FANS may impair efficiency when a linear decision boundary separates data reasonably well. The available remedy is to use both the transformed features and original ones, which may increase computational cost and model complexity. Motivated by these concerns, this paper proposes an efficient nonparametric approach for binary discriminant analysis, called adaptive FANS, integrating augmentation and nonparametric tests. In this procedure, the original features or transformed ones are used selectively to keep the number of features constant. Thus, this procedure avoids the ergodic transformation and reduces error caused by nonparametric estimation and computational complexity. Simulation and real data analysis demonstrate its competitiveness and significant adaptability. Moreover, our approach can be easily extended to other linear frameworks. [ABSTRACT FROM AUTHOR]