Automation of Agromet Advisory by IMD and ICRISAT vs Bharat Forecasting System (BFS) : Efforts to join the missing dots

 

Automation of Agromet Advisory by IMD and ICRISAT vs Bharat Forecasting System (BFS) : Efforts to join the missing dots

The ICRISAT has launched artificial intelligence (AI) and machine learning (ML) to deliver personalized, real-time climate advisory services to farmers. Earlier, IMD, MoES had tried to developed e-advisory for farmers and its automated generation utilizing weather forecast and remote sensing inputs. But still are not in an operation mode at district level advisories formulation. The two things one has to consider that observed verses forecast pattern either it lies in weather or the agromet advisory. Are observed weather has shown any pattern or in the agromet advisories, if yes automation works precisely, if yes automation works precisely, if not replication by automation is only refinement. If you have some patten than AI will work better than any model.

Weather pattern could have forecast probabilities, which are based on the number of ensemble members assigned to each weather pattern. Robert et al. (2022) have reported that the winter dry period weather patterns have the highest forecast skill, closely followed by retreating monsoon weather patterns. In contrast, monsoon onset and break monsoon weather patterns have the lowest forecast skill.

Though the bi-weekly agromet advisory provides farmers with localized agricultural advice based on weather forecasts, crop stage, soil conditions, pest/disease risks, and agricultural best practices, typically issued every 4-5 days.

To have more precise weather forecast the GoI had launched the BFS which may provide highly accurate, real-time weather predictions at an unprecedented 6-kilometer resolution, a significant improvement over the previous 12-kilometer system. This allows for village-level forecasts up to 10 days in advance.

Comments are given by many scholars that one has to consider weather parameters rather than rather variables, use of AI in weather forecasting and agromet advisory. To define a model or define the state parameters are more suitable than variables. The other know dots or difficulties involves in weather forecasting are tropical weather is inherently more chaotic, complex interactions with weather systems, data gaps and randomness. Hence, we need to connect all these dots.

AI and forecasting have limitations

AI cannot reliably predict the toss of a One Day International (ODI) cricket match or any coin toss because the outcome is random by nature. Although the probability is 50 50 just like rain or no rain.

Where chaos and complex interaction exits, precise quantitative forecasting may be not has 95-100 probability. Randomness in output restrict the use of AI, Hopeful for e-advisory and AI forecast would be made automation more precise in this decade.