The Value of Meteorological Information in Agrarian Producers’ Decision Making: Introducing Analytic Decision Models

Cerdá, Emilio; Quiroga, Sonia; Martínez-Juárez, Pablo

doi:10.1007/978-3-319-99462-8_4

Cited by 1 publication

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further compounding this issue, the farmer implementing such a mitigation system must also consider correctly identifying a damaging frost event to avoid expenditures on operating these systems when they are not needed. Thus, accurate meteorological information and frost forecasts can be extremely valuable (Katz et al, 1982 ; Cerdá et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Frost prediction using machine learning and deep neural network models

Talsma

Solander²,

Mudunuru³

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations.

show abstract

Section: Introductionmentioning

confidence: 99%