Taylor Aune
ABSTRACT
On-farm experiments (OFE) are the cornerstone of evaluating interventions on important outcomes like crop yield, disease resistance, soil fertility, and more. However, prospectively planning and implementing all OFE of interest is challenging in resource-constrained settings. In addition, the quality of an OFE is determined by the spatial arrangement of the treatment conditions. Experimental conditions can exist in digital agriculture data, although there may be no information indicating that an experiment took place. We introduce a novel method that can identify potential experimental arrangements on a field using only the spatial information on the experimental conditions of interest. We call this method spatial experiment identification (SPEX-ID). We explain the method in detail this method in a large sample of nearly 90,000 fields from a large commercial digital agriculture database where the intervention of interest was the application of fungicide. From this sample we were able to identify more than 12,000 fields with potential experimental conditions. None of these fields were previously known to contain experimental conditions. We highlight several examples of subfield regions with high-quality experimental arrangements and discuss several avenues for future research.
- Sven Ove Hansson. Farmers' experiments and scientific methodology. European Journal for Philosophy of Science, 9(3):32, May 2019. ISSN 1879-4920. doi: 10.1007/s13194-019-0255-7. URL https://doi.org/10.1007/s13194-019-0255-7.Google Scholar
Cross Ref
- Stuiver, Marian, Leeuwis, Cees, and van der Ploeg, Jan Douwe. The Power of Experience: Farmers' Knowledge and Sustainable Innovations in Agriculture. In Seeds of Transition: Essays on novelty production, niches ans regimes in agriculture., pages 93--118. Van Gorcum, 2004.Google Scholar
- Jock R. Anderson and Gershon Feder. Chapter 44 Agricultural Extension. In R. Evenson and P. Pingali, editors, Handbook of Agricultural Economics, volume 3 of Agricultural Development: Farmers, Farm Production and Farm Markets, pages 2343--2378. Elsevier, January 2007. doi: 10.1016/S1574-0072(06)03044-1. URL https://www.sciencedirect.com/science/article/pii/S1574007206030441.Google ScholarCross Ref
- B. Kelsey Jack. Market inefficiencies and the adoption of agricultural technologies in developing countries. May 2013. URL https://escholarship.org/uc/item/6m25r19c.Google Scholar
- J. D. van der Ploeg. The virtual farmer. Past, present, and future of the Dutch peasantry. Royal van Gorcum, 2003. ISBN 978-90-232-3892-8. URL https://research.wur.nl/en/publications/the-virtual-farmer-past-present-and-future-of-the-dutch-peasantry.Google Scholar
- Julie Ingram and Damian Maye. What Are the Implications of Digitalisation for Agricultural Knowledge? Frontiers in Sustainable Food Systems, 4, 2020. ISSN 2571-581X. URL https://www.frontiersin.org/articles/10.3389/fsufs.2020.00066.Google Scholar
- Elsa T.A. Berthet, Cécile Barnaud, Nathalie Girard, Julie Labatut, and Guillaume Martin. How to foster agroecological innovations? A comparison of participatory design methods. Journal of Environmental Planning and Management, 59(2):280--301, February 2016. ISSN 0964-0568. doi: 10.1080/09640568.2015.1009627. URL https://doi.org/10.1080/09640568.2015.1009627. Publisher: Routledge _eprint:https://doi.org/10.1080/09640568.2015.1009627.Google ScholarCross Ref
- Chloé Alexandre, Léa Tresch, Julien Sarron, Jéremy Lavarenne, Gaspard Bringer, Hamza Rkha Chaham, Hamza Bendahou, Sofia Carmeni, Philippe Borianne, Jean-Mathias Koffi, and Emile Faye. Creating shared value(s) from On-Farm Experimentation: ten key lessons learned from the development of the SoYield® digital solution in Africa. Agronomy for Sustainable Development, 43(3):38, May 2023. ISSN 1773-0155. doi: 10.1007/s13593-023-00888-7. URL https://doi.org/10.1007/s13593-023-00888-7.Google ScholarCross Ref
- Kelly Bronson and Irena Knezevic. Big Data in food and agriculture. Big Data & Society, 3(1):2053951716648174, June 2016. ISSN 2053-9517. doi: 10.1177/2053951716648174. URL https://doi.org/10.1177/2053951716648174. Publisher: SAGE Publications Ltd.Google ScholarCross Ref
- Andreas Kamilaris, Andreas Kartakoullis, and Francesc X. Prenafeta-Boldú. A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143:23--37, December 2017. ISSN 0168-1699. doi: 10.1016/j.compag.2017.09.037. URL https://www.sciencedirect.com/science/article/pii/S0168169917301230.Google ScholarDigital Library
- Keogh, M and Henry, M. The Implications of Digital Agriculture and Big Data for Australian Agriculture. Technical report, Australian Farm Institute, 2016. URL https://www.crdc.com.au/sites/default/files/pdf/Big_Data_Report_web.pdf. ISBN 978-1-921808-38-8 (Print and Web).Google Scholar
- Sjaak Wolfert, Lan Ge, Cor Verdouw, and Marc-Jeroen Bogaardt. Big Data in Smart Farming -- A review. Agricultural Systems, 153:69--80, May 2017. ISSN 0308-521X. doi: 10.1016/j.agsy.2017.01.023. URL https://www.sciencedirect.com/science/article/pii/S0308521X16303754.Google ScholarCross Ref
- Alfons Weersink, Evan Fraser, David Pannell, Emily Duncan, and Sarah Rotz. Opportunities and Challenges for Big Data in Agricultural and Environmental Analysis. Annual Review of Resource Economics, 10(1):19--37, 2018. doi:10.1146/annurev-resource-100516-053654. URL https://doi.org/10.1146/annurev-resource-100516-053654. _eprint: https://doi.org/10.1146/annurev-resource-100516-053654.Google ScholarCross Ref
- Sander J. C.Janssen, Cheryl H. Porter, Andrew D. Moore, Ioannis N. Athanasiadis, Ian Foster, James W. Jones, and John M. Antle. Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. Agricultural Systems, 155:200--212, July 2017. ISSN 0308-521X. doi: 10.1016/j.agsy.2016.09.017. URL https://www.sciencedirect.com/science/article/pii/S0308521X16305637.Google ScholarCross Ref
- H. Maat. The history and future of agricultural experiments. NJAS - Wageningen Journal of Life Sciences, 57(3):187--195, February 2011. ISSN 1573-5214. doi: 10.1016/j.njas.2010.11.001. URL https://www.sciencedirect.com/science/article/pii/S1573521410000461.Google ScholarCross Ref
- Sheng-sheng Wang, Da-you Liu, Xin-ying Wang, and Jie Liu. Spatial Reasoning Based Spatial Data Mining for Precision Agriculture. In Heng Tao Shen, Jinbao Li, Minglu Li, Jun Ni, and Wei Wang, editors, Advanced Web and Network Technologies, and Applications, Lecture Notes in Computer Science, pages 506--510, Berlin, Heidelberg, 2006. Springer. ISBN 978-3-540-32435-5. doi: 10.1007/11610496_65.Google ScholarDigital Library
- M. J. Pringle, T. F. A. Bishop, R. M. Lark, B. M. Whelan, and A. B. McBratney. The Analysis of Spatial Experiments. In M.A. Oliver, editor, Geostatistical Applications for Precision Agriculture, pages 243--267. Springer Netherlands, Dordrecht, 2010. ISBN 978-90-481-9133-8. doi: 10.1007/978-90-481-9133-8_10. URL https://doi.org/10.1007/978-90-481-9133-8_10.Google ScholarCross Ref
- M. Fagroud and M. Van Meirvenne. Accounting for Soil Spatial Autocorrelation in the Design of Experimental Trials. Soil Science Society of America Journal, 66(4):1134--1142, 2002. ISSN 1435-0661. doi: 10.2136/sssaj2002.1134. URL https://onlinelibrary.wiley.com/doi/abs/10.2136/sssaj2002.1134. _eprint:https://onlinelibrary.wiley.com/doi/pdf/10.2136/sssaj2002.1134.Google ScholarCross Ref
- Pierre Legendre, Mark R. T. Dale, Marie-Josée Fortin, Philippe Casgrain, and Jessica Gurevitch. Effects of Spatial Structures on the Results of Field Experiments. Ecology, 85(12):3202--3214, 2004. ISSN 1939-9170. doi: 10.1890/03-0677. URL https://onlinelibrary.wiley.com/doi/abs/10.1890/03-0677. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1890/03-0677.Google ScholarCross Ref
- Teodor Fredriksson, David Issa Mattos, Jan Bosch, and Helena Holmström Olsson. Data Labeling: An Empirical Investigation into Industrial Challenges and Mitigation Strategies. In Product-Focused Software Process Improvement: 21st International Conference, PROFES 2020, Turin, Italy, November 25-27, 2020, Proceedings, pages 202--216, Berlin, Heidelberg, November 2020. Springer-Verlag. ISBN 978-3-030-64147-4. doi: 10.1007/978-3-030-64148-1_13. URL https://doi.org/10.1007/978-3-030-64148-1_13.Google ScholarDigital Library
- IBM. IBM AI and Cloud Technology Helps Agriculture Industry Improve the World's Food and Crop Supply, 2019. URL https://newsroom.ibm.com/2019-05-22-IBM-AI-and-Cloud-Technology-Helps-Agriculture-Industry-Improve-the-Worlds-Food-and-Crop-Supply.Google Scholar
- Keith H Coble, Ashok K Mishra, Shannon Ferrell, and Terry Griffin. Big Data in Agriculture: A Challenge for the Future. Applied Economic Perspectives and Policy, 40(1):79--96, 2018. ISSN 2040-5804. doi: 10.1093/aepp/ppx056. URL https://onlinelibrary.wiley.com/doi/abs/10.1093/aepp/ppx056. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1093/aepp/ppx056.Google ScholarCross Ref
- Sunderrajan Krishnan and A. G. Journel. Spatial Connectivity: From Variograms to Multiple-Point Measures. Mathematical Geology, 35(8):915--925, November 2003. ISSN 1573-8868. doi: 10.1023/B:MATG.0000011585.73414.35. URL https://doi.org/10.1023/B:MATG.0000011585.73414.35.Google ScholarCross Ref
- G. M. Laslett, A. B. McBratney, P. J. Pahl, and M. F. Hutchinson. Comparison of several spatial prediction methods for soil pH. Journal of Soil Science, 38(2):325--341, 1987. ISSN 1365-2389. doi: 10.1111/j.1365-2389.1987.tb02148.x. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2389.1987.tb02148.x. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2389.1987.tb02148.x.Google ScholarCross Ref
- H. M. van Es and C. L. van Es. Spatial Nature of Randomization and Its Effect on the Outcome of Field Experiments. Agronomy Journal, 85(2):420--428, 1993. ISSN 1435-0645. doi: 10.2134/agronj1993.00021962008500020046x. URL https://onlinelibrary.wiley.com/doi/abs/10.2134/agronj1993.00021962008500020046x. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.2134/agronj1993.00021962008500020046x.Google ScholarCross Ref
- Raegan Hoefler, Pablo González-Barrios, Madhav Bhatta, Jose A. R. Nunes, Ines Berro, Rafael S. Nalin, Alejandra Borges, Eduardo Covarrubias, Luis Diaz-Garcia, Martin Quincke, and Lucia Gutierrez. Do Spatial Designs Outperform Classic Experimental Designs? Journal of Agricultural, Biological and Environmental Statistics, 25(4):523--552, December 2020. ISSN 1537-2693. doi: 10.1007/s13253-020-00406-2. URL https://doi.org/10.1007/s13253-020-00406-2.Google ScholarCross Ref
- Kiersten A. Wise, Damon Smith, Anna Freije, Daren S. Mueller, Yuba Kandel, Tom Allen, Carl A. Bradley, Emmanuel Byamukama, Martin Chilvers, Travis Faske, Andrew Friskop, Clayton Hollier, Tamra A. Jackson-Ziems, Heather Kelly, Bob Kemerait, Paul Price, Alison Robertson, and Albert Tenuta. Meta-analysis of yield response of foliar fungicide-treated hybrid corn in the United States and Ontario, Canada. PLoS ONE, 14(6):e0217510, June 2019. ISSN 1932-6203. doi:10.1371/journal.pone.0217510. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550426/.Google ScholarCross Ref
- Jay Ram Lamichhane, Ming Pei You, Véronique Laudinot, Martin J. Barbetti, and Jean-Noël Aubertot. Revisiting Sustainability of Fungicide Seed Treatments for Field Crops. Plant Disease, 104(3):610--623, March 2020. ISSN 0191-2917. doi: 10.1094/PDIS-06-19-1157-FE. URL https://apsjournals.apsnet.org/doi/full/10.1094/PDIS-06-19-1157-FE. Publisher: Scientific Societies.Google ScholarCross Ref
- Carsten F. Dormann, Jana M. McPherson, Miguel B. Araújo, Roger Bivand, Janine Bolliger, Gudrun Carl, Richard G. Davies, Alexandre Hirzel, Walter Jetz, W. Daniel Kissling, Ingolf Kühn, Ralf Ohlemüller, Pedro R. Peres-Neto, Björn Reineking, Boris Schröder, Frank M. Schurr, and Robert Wilson. Methods to Account for Spatial Autocorrelation in the Analysis of Species Distributional Data: A Review. Ecography, 30(5):609--628, 2007. ISSN 0906-7590. URL https://www.jstor.org/stable/30244511. Publisher: [Nordic Society Oikos, Wiley].Google ScholarCross Ref
- Luc Anselin. Local Indicators of Spatial Association---LISA. Geographical Analysis, 27(2):93--115, 1995. ISSN 1538-4632. doi: 10.1111/j.1538-4632.1995.tb00338.x. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1538-4632.1995.tb00338.x._eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1995.tb00338.x.Google ScholarCross Ref
- Martin Herold, Joseph Scepan, and Keith C Clarke. The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses. Environment and Planning A: Economy and Space, 34(8):1443--1458, August 2002. ISSN 0308-518X. doi: 10.1068/a3496. URL https://doi.org/10.1068/a3496. Publisher: SAGE Publications Ltd.Google ScholarCross Ref
- Daniel Arribas-Bel and Martin Fleischmann. Spatial Signatures - Understanding (urban) spaces through form and function. Habitat International, 128:102641, October 2022. ISSN 0197-3975. doi: 10.1016/j.habitatint.2022.102641. URL https://www.sciencedirect.com/science/article/pii/S0197397522001382.Google ScholarCross Ref
- Elizabeth Wentz. Shape analysis in GIS. Auto-Carto, 13, January 1997.Google Scholar
Index Terms
- Spatial experiment identification (SPEX-ID): a method to identify experimental conditions from spatial information in digital agricultural data and beyond
Recommendations
Mining Spatial Co-occurrence of Drought Events from Climate Data of India
ICDMW '10: Proceedings of the 2010 IEEE International Conference on Data Mining WorkshopsIncreasingly in the recent past, the focus on climate change is moving towards the understanding of the occurrence, both in magnitude and frequency, of extreme climatic events like droughts and floods. In this paper, an effort has been made towards ...
Research of Spatial Pattern for Cultivated Land Quality in Henan Province Based on Spatial Autocorrelation
Artificial Intelligence and SecurityAbstractThe quality of cultivated land determines the productivity and level of regional development, and it also directly affects the country’s food security and ecological security. In order to improve the quality of cultivated land and the efficiency ...
Analysis on Spatial Pattern Evolution of Cultivated Land in Urban Area Based on Spatial Autocorrelation Analysis – A Case Study of Luoyang City
Artificial Intelligence and SecurityAbstractCultivated land is the basis of human survival and security. Taking cultivated land of Luoyang as the research object, based on GIS technology, using landscape pattern index method, kernel density analysis and spatial autocorrelation analysis, ...
Comments