Importance of weather monitoring for agricultural decision‐making – an exploratory behavioral study for Oklahoma Mesonet

Ziolkowska, Jadwiga R.; Zubillaga, Jesus

doi:10.1002/jsfa.9027

Cited by 6 publications

(1 citation statement)

References 31 publications

(55 reference statements)

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“…ese results support prior research finding that producers who are already users of monitoring information see the greatest value in the information [40], in part because those not currently using the information may be "unconsciously incompetent" with regard to the potential use of the information [41].…”

supporting

confidence: 82%

Drought Early Warning and the Timing of Range Managers’ Drought Response

Haigh

Otkin

Mucia

et al. 2019

Advances in Meteorology

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The connection between drought early warning information and the timing of rangeland managers’ response actions is not well understood. This study investigates U.S. Northern Plains range and livestock managers’ decision-making in response to the 2016 flash drought, by means of a postdrought survey of agricultural landowners and using the Protective Action Decision Model theoretical framework. The study found that managers acted in response to environmental cues, but that their responses were significantly delayed compared to when drought conditions emerged. External warnings did not influence the timing of their decisions, though on-farm monitoring and assessment of conditions did. Though this case focused only on a one-year flash drought characterized by rapid drought intensification, waiting to destock pastures was associated with greater losses to range productivity and health and diversity. This study finds evidence of unrealized potential for drought early warning information to support proactive response and improved outcomes for rangeland management.

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supporting

confidence: 82%

Drought Early Warning and the Timing of Range Managers’ Drought Response

Haigh

Otkin

Mucia

et al. 2019

Advances in Meteorology

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Exploration of sub-field microclimates and winter temperatures: Implications for precision agriculture

et al. 2021

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Deep learning tool: reconstruction of long missing climate data based on spatio-temporal multilayer perceptron

Xu,

Zhang,

Zhang

et al. 2024

Theor Appl Climatol

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Long-term monitoring of climate data is significant for grasping the law and development trend of climate change and guaranteeing food security. However, some weather stations lack monitoring data for even decades. In this study, 62 years of historical monitoring data from 105 weather stations in Xinjiang were used for missing sequence prediction, validating proposed data reconstruction tool. First of all, study area was divided into three parts according to the climatic characteristics and geographical locations. A spatio-temporal multilayer perceptron (MLP) was established to reconstruct meteorological data with three time scales (Short term, cycle and long term) and one spatio dimension as inputing (rolling predictions, one step predicts one day), filling in long sequence blank data. By designing an end-to-end model to autonomously detect the locations of missing data and make rolling predictions,we obtained complete meteorological monitoring data of Xinjiang from 1961 to 2022. Seven kinds of parameter reconstructed include maximum temperature (Max_T), minimum temperature (Min_T), mean temperature (Ave _ T), average water vapor pressure (Ave _ WVP), relative humidity (Ave _ RH), average wind speed (10 m Ave _ WS), and sunshine duration (Sun_H). Contrasted the prediction accuracy of the model with general MLP and LSTM, results shows that, in the seven types of parameters, designed spatio-temporal MLP decreases MAE and MSE by 7.61% and 4.80% respectively. The quality of reconstructed data was evaluated by calculating correlation coefficient with the monitored sequences of nearest station,determining the applicable meteorological parameters of the model according to the results. Results show that,proposed model reached satisfied average correlation coefficient for Max_T, Min_T, Ave _ T and Ave _ WVP parameters are 0.969, 0.961, 0.971 and 0.942 respectively. The average correlation coefficient of Sun_H and Ave _ RH are 0.720 and 0.789. Although it is difficult to predict extreme values, it can still capture the period and trend; the reconstruction effect of 10 m Ave _ WS is poor, with the average similarity of 0.488. Proposed method is applicable to reconstruct Max_T, Min_T, Ave _ T and Ave _ WVP, but not recommended to reconstruct Sun_H, Ave _ RH and Ave _ WS.

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Importance of weather monitoring for agricultural decision‐making – an exploratory behavioral study for Oklahoma Mesonet

Cited by 6 publications

References 31 publications

Drought Early Warning and the Timing of Range Managers’ Drought Response

Drought Early Warning and the Timing of Range Managers’ Drought Response

Exploration of sub-field microclimates and winter temperatures: Implications for precision agriculture

Deep learning tool: reconstruction of long missing climate data based on spatio-temporal multilayer perceptron

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