{
  "abstract": "Growers and advisers increasingly run large strip trials on their own paddocks, but the rules used to design them come from small-plot research. We simulated strip trials on a spatially autocorrelated field, varying trial length (L), replication (r), randomised or systematic allocation, and long-strip or stacked layout, for a nitrogen response that was uniform or varied across within-field pseudo-environments (PEs). Analyses of strip-plot means and of yield pixels were first checked for Type I error, and designs were then compared by the smallest response that a valid analysis detects with 80% power. Follow-up runs added gaps, unequal strip lengths, trends, spatially varying responses and alternative field structures; more than 3 million models were fitted. Treating pixels as independent replicates gave false positives in 32-40% of null trials. Replication was the strongest design lever: the detectable response scaled as $r^{-0.60}$ to $r^{-1.02}$ but only as $L^{-0.45}$ or more slowly, and at equal plot area more, shorter replicates won all 66 comparisons. With complete data, strip means with an autoregressive residual were the most precise analysis; when strips contained gaps, pixel-level spatial models with a strip effect halved the detectable response, while the spatial analysis of strip means became liberal. For PE-varying responses, spatial PE models detected per-PE effects 33-46% smaller than PE means without a spatial term. Randomisation did not improve precision but kept tests valid (systematic allocation: Type I error up to 23%), whereas systematic allocation gave more accurate response maps. The results are condensed into a design table and a detectable-gain chart for use before a trial is sown.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.38297",
  "author_total": 5,
  "authors": [
    {
      "name": "Zhanglong Cao"
    },
    {
      "name": "Suman Rakshit"
    },
    {
      "name": "Barbara Kachigunda"
    },
    {
      "name": "Julia Easton"
    },
    {
      "name": "Mark Gibberd"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7s397v3bbdtmp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.38297",
  "fulltext_url": null,
  "id": "7s397v3bbdtmp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.38297",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.OT"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.38297",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7s397v3bbdtmp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:41Z",
      "native_id": "2610.38297",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.38297"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Statistical Elements of a Successful Farm-scale Trial",
  "title_text": "Statistical Elements of a Successful Farm-scale Trial",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/7s397v3bbdtmp",
  "venue": "arXiv",
  "versions": []
}