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Predicting Post-Harvest Soil Test Values in Hybrid Rice (Oryza Sativa L.)–Wheat (Triticum Aesitvum L.) Cropping Sequence Using a Multivariate Analysis Technique

Communications in Soil Science and Plant Analysis, ISSN: 1532-2416, Vol: 50, Issue: 13, Page: 1624-1639
2019
  • 7
    Citations
  • 0
    Usage
  • 13
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    7
    • Citation Indexes
      7
  • Captures
    13

Article Description

Knowledge of the initial soil fertility status is very crucial to make the soil test-based fertilizer recommendations and therefore it is necessary to develop alternative techniques to predict the post-harvest soil test values than analyzing the soils after every crop. The study was done to develop multiple linear regression (MLR) models to predict soil available nitrogen, phosphorus and sulfur in the hybrid rice-wheat cropping sequence. The post-harvest soil test values were considered as the dependent variable and initial soil nutrients applied nutrient through fertilizer and farmyard manure and grain yield as independent variables. In general, the accuracy of prediction for the calibration and validation models using the single year and two-year data model was significant and had a coefficient of determination was ≥0.75. Although the performance of MLR model to predict post-harvest soil N, P and S after the individual crop was better than that after whole rice-wheat cropping sequence, predictions of the post-rice-wheat sequence of soil N, P and S also had acceptable levels of accuracy. Thus, the concept of the using the MLR-based models to predict the post-harvest soil test values could be used in hybrid rice-wheat cropping sequence to make the soil test-based fertilizer recommendations to the individual crops or whole cropping sequence.

Bibliographic Details

G. R. Mahajan; B. Das; R. N. Pandey; S. C. Datta; Dinesh Kumar; R. N. Sahoo; K. P. Patel; Dayesh Murgaonkar

Informa UK Limited

Agricultural and Biological Sciences

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