Agronōmics: transforming crop science through digital technologies

Sylvester‐Bradley, R.; Kindred, D. R.; Marchant, Ben P.; Rudolph, Sebastian; Roques, S.; Calatayud, Ángeles; Clarke, Sarah; Gillingham, Vincent

doi:10.1017/s2040470017001029

Cited by 7 publications

(11 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, social learning at several scales generates new knowledge 3,7,11,15 . Within OFE, co-learning between partners is key, from the co-design of experiments to the interpretation of results 25,26 . Crucially, anchoring co-learning in the farm's data provides tangible focus.…”

Section: Ofe Embeds Research In Farm Managementmentioning

confidence: 99%

“…Spatial and temporal variations in crop and livestock production are far greater than trial treatment effects, the stability of which are highly sensitive to the scale, boundaries and descriptors used 18,19,[32][33][34]39 . Furthermore, the statistical significance criteria used by scientists provide no indication as to the scope, meaningfulness or local usefulness of results, leaving to farmers the difficult and risky task of adapting recommendations 4,14,18,21,22,25,37 . OFE overcomes these problems by embedding experiments in farmers' management, grounding the experiments locally at scales that are meaningful to them 20 .…”

Section: Progressing Experimentationmentioning

confidence: 99%

“…Sourcing innovation directly from farmers by supporting their own problem-solving processes stems from a recognized need for decentralized, inclusive and networked approaches to agricultural research, development and extension [3][4][5][6][7][8] . Disciplines as distinct as agronomy, ecology, geography, anthropology, engineering, business and management are reaching this consensus and arguing for collective action, yet institutional practices have so far changed little 2,5,6,8,10,11,14,15,17,20,21,25,[29][30][31]38,39,41,44,45 .…”

Section: Opening Innovationmentioning

confidence: 99%

See 2 more Smart Citations

On-Farm Experimentation to transform global agriculture

et al. 2021

View full text Add to dashboard Cite

This is a peer-reviewed, post-print (final draft post-refereeing) version of the following published document, Conditions of use Articles published within the Springer Nature group of companies which are made available through academic repositories remain subject to copyright. The following restrictions on use of such articles apply: Academic research only 1. Archived content may only be used for academic research. Any content downloaded for text based experiments should be destroyed when the experiment is complete. Use must not be for Commercial Purposes 2. Archived content may not be used for purposes that are intended for or directed towards commercial advantage or monetary compensation by means of sale, resale, licence, loan, transfer or any other form of commercial exploitation ("Commercial Purposes"). Wholesale re-publishing is prohibited 3. Archived content may not be published verbatim in whole or in part, whether or not this is done for Commercial Purposes, either in print or online. 4. This restriction does not apply to reproducing normal quotations with an appropriate citation. In the case of text-mining, individual words, concepts and quotes up to 100 words per matching sentence may be used, whereas longer paragraphs of text and images cannot (without specific permission from Springer Nature). Moral rights 5. All use must be fully attributed. Attribution must take the form of a link-using the article DOI-to the published article on the journal's website. 6. All use must ensure that the authors' moral right to the integrity of their work is not compromised. Third party content 7. Where content in the document is identified as belonging to a third party, it is the obligation of the user to ensure that any use complies with copyright policies of the owner. Use at own risk 8.

show abstract

Section: Ofe Embeds Research In Farm Managementmentioning

confidence: 99%

Section: Progressing Experimentationmentioning

confidence: 99%

Section: Opening Innovationmentioning

confidence: 99%

See 1 more Smart Citation

On-Farm Experimentation to transform global agriculture

et al. 2021

View full text Add to dashboard Cite

show abstract

“…On-farm experimentation (OFE) has been proposed as a process to enhance the adoption of digital technologies in agriculture, develop farmer competence, and improve profitability [14][15][16], and it is becoming widespread [17,18]. OFE is farmer-centric, where farmers work with consultants and/or researchers to design and implement large-scale experiments on their farms to test management practices.…”

Section: Introductionmentioning

confidence: 99%

“…However, because the same methods of analysis can be applied to any continuous measurement, we discuss yield measurements from hereon. Experiments might compare discrete treatments such as the variety [19] or types of soil amelioration, quantify response to different rates of a single treatment such as fertiliser or fungicide [16,20,21], or consider multiple treatments. Various designs are used, including strip trials [22][23][24][25][26][27], nitrogen-rich strips [28], and checkerboards [29,30].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application

et al. 2020

View full text Add to dashboard Cite

On-farm experimentation (OFE) is a farmer-centric process that can enhance the adoption of digital agriculture technologies and improve farm profitability and sustainability. Farmers work with consultants or researchers to design and implement experiments using their own machinery to test management practices at the field or farm scale. Analysis of data from OFE is challenging because of the large spatial variation influenced by spatial autocorrelation that is not due to the treatment being tested and is often much larger than treatment effects. In addition, the relationship between treatment and yield response may also vary spatially. We investigate the use of geographically weighted regression (GWR) for analysis of data from large on-farm experiments. GWR estimates local regressions, where data are weighted by distance from the site using a distance-decay kernel. It is a simple approach that can be easily explained to farmers and their agronomic advisors. We use simulated data to test the ability of GWR to separate yield variation due to treatment from any underlying spatial variation in yield that is not due to treatment; show that GWR kernel bandwidth can be based on experimental design to accurately separate the underlying spatial variability from treatment effects; and demonstrate a step-wise model selection approach to determine when the response to treatment is global across the experiment or locally varying. We demonstrate our recommended approach on two large-scale experiments conducted on farms in Western Australia to investigate grain yield response to potassium fertiliser. We discuss the implications of our results for routine practical application to OFE and conclude that GWR has potential for wide application in a semi-automated manner to analyse OFE data, improve farm decision-making, and enhance the adoption of digital technologies.

show abstract

Modelling the impact of cabbage stem flea beetle larval feeding on oilseed rape lodging risk

Wilkinson,

Coston,

Berry

et al. 2024

Pest Management Science

View full text Add to dashboard Cite

BackgroundCabbage stem flea beetle (CSFB, Psylliodes chrysocephala L.) is a major pest of oilseed rape (OSR, Brassica napus L.) in the UK and low availability of effective chemical control has increased the need for integrated pest management approaches. The risk of OSR to lodging is strongly related to stem strength, however, the impact of CSFB larval tunnelling on stem strength and subsequent risk to stem lodging is unknown. The study investigated this by applying the Generalised Crop Lodging Model to conventionally grown OSR crops scored for varying levels of CSFB larval tunnelling. Lodging risk mitigation strategies including Plant Growth Regulators (PGR) and varying nitrogen regimes were tested under high CSFB larval pressure.ResultsStems of OSR plants were categorised by the proportion of visual damage (<5%; 5–25%; 26–50%; 51–75%; 75–100%). Stems of 26–50% damage had significantly lower breaking strengths and diameters compared to plants that scored <5%, with the associated reduction in stem failure windspeed equivalent to an order of magnitude increase in the risk of a lodging event occurring in the UK. PGR use reduced plant height and subsequently lodging risk variably across the sites.ConclusionEstimating the proportion of stem tunnelling alongside larval pressure may be a useful tool in considering the contribution of CSFB pressure to lodging risk. The research demonstrates that the use of canopy management principles to optimise canopy size through nitrogen management and PGR use may help offset increased lodging risk caused by CSFB tunnelling.This article is protected by copyright. All rights reserved.

show abstract

Agronōmics: transforming crop science through digital technologies

Cited by 7 publications

References 8 publications

On-Farm Experimentation to transform global agriculture

On-Farm Experimentation to transform global agriculture

Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application

Modelling the impact of cabbage stem flea beetle larval feeding on oilseed rape lodging risk

Contact Info

Product

Resources

About