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Precision Agriculture

, Volume 4, Issue 1, pp 69–86 | Cite as

Sampling Strategies for Mapping ‘Within-field’ Variability in the Dry Matter Yield and Mineral Nutrient Status of Forage Grass Crops in Cool Temperate Climes

  • Crawford Jordan
  • Z. Shi
  • John S. Bailey
  • Alex J. Higgins
Article

Abstract

In the absence of suitable technology to measure and map the dry matter (DM) yield distributions of forage grass crops within individual fields, a ‘manual’ procedure of yield mapping has been developed. Samples of herbage are collected just prior to each silage harvest from known grid points within a field, and sward DM yields at each point are predicted from the mineral composition of the herbage, using empirical mathematical models. Yield maps (and maps of sward nutrient status) are then produced by kriging interpolation between the point data. To make the most efficient use of time and resources, however, sampling intensity needs to be kept to the absolute minimum necessary for interpolation purposes. The aim of the present study was to examine the spatial variability in sward DM yield and mineral nutrient status in a large grass silage field under a three-cut system, and devise ‘optimal’ sampling strategies for mapping the distributions of these parameters at each cut. Herbage samples were collected from the field, prior to each harvest, at 25 m intervals in a regular rectangular grid to provide databases of herbage nutrient contents and DM yields. Different data combinations were abstracted from these databases for comparison purposes, and ordinary kriging used to produce interpolated maps of DM yield and sward N, P, K and S statuses. The results suggested that a sampling density of just seven samples per hectare was adequate for estimating the ‘true’ population means of sward DM yield and sward N, P, K, and S statuses. For mapping purposes, it was found that the best compromise between interpolation accuracy and sampling efficiency was to collect herbage samples in a 35.4 m×35.4 m equilateral triangular sampling pattern.

Keywords

Ordinary Kriging Mineral Composition Yield Distribution Data Combination Mapping Purpose 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Crawford Jordan
    • 1
  • Z. Shi
    • 2
  • John S. Bailey
    • 3
  • Alex J. Higgins
    • 3
  1. 1.Department of Agricultural and Environmental ScienceThe Queen's University of Belfast, Newforge LaneBelfastUK
  2. 2.Department of Soil Science and AgrochemistryZhejiang UniversityHangzhouPeople's Republic of China
  3. 3.Department of Agriculture and Rural Development for Northern Ireland, Agricultural and Environmental Science DivisionNewforge LaneBelfastUK

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