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Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience

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Title Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience
 
Creator Cock, James
Jiménez, Daniel
Dorado, Hugo
Oberthur, Thomas
 
Subject risk management
machine learning
agricultural practices
operations research
crop production
crop management
 
Description The potential for operations research with farmer supplied data coupled with machine learning to improve crop management is explored through a series of case studies from developing countries. The information provided by the farmers ranged from solely yield to a description of the management of the crop and some details of the growth environment. The climate or weather conditions of the georeferenced farms were estimated from publicly available data bases. Two principle analytical approaches were used. The first benchmarks crop performance against farmers practices and the second establishes relatively Homogenous Environmental Conditions (HECs) in which the variation in crop response is due to variation in management practices and not to spatiotemporal variation in biophysical factors. Both approaches depend on large amounts of data which can only realistically be obtained from records of on farm experiences using an operations research focus. Machine learning effectively defined HECs for crops with limited prior knowledge on the biophysical factors that influence crop response. The definition of HECs facilitated the identification of either individual farmers who managed their crops well within individual HECs or combinations of management practices well suited to the specific spatiotemporal environmental conditions. This opens the way for farmers to learn better agricultural practices from others in the same HEC. Variation in yield and fertilizer response was associated with variation in the El Niño Southern Oscillation (ENSO) patterns up to 24 months before the harvest: this offers the opportunity for farmers to minimizes risk, based on ENSO predictions, even when they have no information on how ENSO influences their weather patterns. Despite concerns about the quality of farmer data, the consistency of the analyses suggests that even relatively crude production data from individual farms analysed with machine learning can provide useful guidelines for crop management. Limited variation in management on farmers’ fields may limit the ability to identify optimal practices, however, this constraint can be partially obviated by superimposing varied management practices on farmers’ fields. The use of operations research combined with machine learning complements, rather than replaces, traditional research methodologies. Furthermore, the approach must be used carefully with emphasis on the dangers of extrapolation to circumstances that are not encompassed by the original data sets.
 
Date 2023-06
2023-04-05T14:24:44Z
2023-04-05T14:24:44Z
 
Type Journal Article
 
Identifier Cock, J.; Jiménez, D.; Dorado, H.; Oberthur, T. (2023) Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience. Current Opinion in Environmental Sustainability 62: 101278. ISSN: 1877-3435
1877-3435
https://hdl.handle.net/10568/129905
https://doi.org/10.1016/j.cosust.2023.101278
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format 101278
application/pdf
 
Publisher Elsevier BV
 
Source Current Opinion in Environmental Sustainability