The phrase "agricultural decision" is correct and usable in written English.
It can be used when referring to choices or determinations made in the context of agriculture, farming, or related fields.
Example: "The committee is tasked with making an agricultural decision that will impact local farmers for years to come."
Alternatives: "farming choice" or "agriculture-related decision".
This study finds a plethora of agricultural decision support tools in operation in the UK, yet, like other studies, shows that their uptake is low.
This newly developed system was evaluated as an expert agricultural decision support for sustainable water resource management case study in Australia at Tasmania with promising results.
NEWA is an online agricultural decision support system that uses real time weather data, streamed over the internet from 573 weather stations throughout the Northeast, Midwest and mid-Atlantic.
In March, ten data streams (table 1, figure 1) were established to link NYSM-generated weather data to the NEWA online agricultural decision support system, available for use by agricultural stakeholders across New York State.
In developing this architecture, which is very useful for agricultural decision support systems, we considered web based large-scale dynamic data mining, contextual knowledge extraction, and integrated knowledge representation methods.
Therefore, it is critical to optimally integrate weather/climate forecasts released at different time scales in different formats, and to produce daily weather sequences for a whole crop growing season in order to utilize process-based crop models for pre-season yield forecasting or other agricultural decision supports.
This would cover much of everyday food consumption (e.g., eat this piece of cake now vs. postpone eating it), to agricultural farming decisions (e.g., how much of the underground water reserves to use today) as well as health-related decision making (e.g., smoking decisions).
Use of ENSO-related climate information in agricultural decision-making in Argentina: a pilot experience.
However, agricultural decision-makers need ad-hoc KPIs issued from low-cost sensors.
The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making.
Delineation and characterization of agroecozones would greatly enhance agricultural decision-making and management, as well as the extrapolation of experiment station research and field trials to forms and landscapes of similar agronomic behavior.
