Harvest operations for a variety of agricultural commodities are substantially influenced by environmental factors, such as the weather.  While some weather conditions, such as precipitation, may create an obvious deterrent to harvest operations, more benign daily weather characteristics also play subtle yet significant roles.  For example, many commodities require that the harvested product be at or below a product-dependent moisture threshold before they can be stably stored at ambient temperatures (at least without taking specific steps to keep the product stable, such as the maintenance of a constant airflow through the product).  On the other extreme, delaying harvest for too long can result in the crop becoming overly dry, potentially exposing seeds to damage during the threshing process, or removing permissible water weight from the product.  Such an occurrence of delayed harvest may result in lower crop revenue, since payments are often based on mass.  Similarly, crop temperature thresholds may be a major consideration for long-term storage of some crops, for example tuberous crops such as potatoes and sugar beets, where cold conditions are advantageous. 
The harvest operation itself is also often sensitive to plant, product, and soil moisture and temperature levels.  For instance, green plants, or even deceased plants with a heightened moisture level, often create difficulty for harvest operations that are based on the use of a threshing action to separate the seed (or other product) from the parent plant or stalk.  This can result in both yield loss due to un-threshed seeds passing through and out of the harvester, and seed damage due to the repeated or harsh threshing action that may be required.  Harvesting crops at a higher moisture content than is typical for long-term storage has short-term advantages, for example for livestock feed, however, yield loss may occur due to spoilage or mold if the grain moisture is too high while stored, or damage may occur to storage structures if excessive moisture seeps from the agricultural product.  Additionally, yield or grain nutrient loss can result from overly dry plants or plant parts (such as a corn cob or corn husk), for example, when harvesting corn for silage, earlage, snaplage, for high-moisture corn grain, or for other types of livestock feed.  Frozen or excessively wet soils can also inhibit harvest operations for various crops, depending upon the harvest mechanism for the particular crop.  Each of the product, plant, and soil moisture and temperature therefore impact both the timing and viability of harvest operations, and all of these qualities are highly influenced by complex interactions between plant and soil characteristics and environmental conditions. 
As global agricultural operations continue to grow in size, the practicality of in-situ monitoring of field conditions on a regular basis is becoming increasingly diminished.  Further, the often substantial equipment and labor resources involved in harvest operations are not easily moved across significant distances in an effort to find fields with viable or more favorable harvest conditions.  The ability to both diagnose and predict the viability of harvest operations in a potentially remote field is therefore of increasing importance to the management of modern farm operations.  Also, production agriculture is often a capital-intensive business with very thin relative profit margins.  The ability to more effectively manage the logistics associated with deployment of a farm operation's equipment and human resources is therefore becoming increasingly critical to profitability and long-term viability of the farm itself. 
In part because agricultural research globally is largely carried out by institutions with local or regional focus, agronomic models are often based on sample datasets that are limited in size and/or geographic representativeness, with less than ideal documentation of (or accommodation for) associated weather and environmental conditions.  Because of this, there are very few models that can be picked up and applied to other locations and timeframes without a potentially substantial loss in model accuracy.  Models for the same processes can often lead to diametrically opposed conclusions when applied at differing locations because of model shortcomings that are due to a lack of understanding of the extent to which localized influences impact the associated processes during the development of the model. 
Existing solutions do not provide a sufficient framework for utilizing weather analysis and prediction to accurately diagnose field-level weather conditions for precision agriculture to overcome the challenges above.  Accordingly, there is a strong need not found in the existing art for a system and method that provides an improved process for application of weather information in agronomic modeling to produce a better understanding of farm and harvest operations.  There is also a need not found in the existing art for support tools designed to provide real-time assessments of weather conditions and the impact on crops, plants, soils, and resulting agricultural products to enable improvements in farm and harvest operations.