Proper assessment of the agricultural land is an important factor in ensuring food security and effective crop growth. Not only does the GIS technology help improve crop productivity, but it has proven to be an ideal way to ensure sustainable planting. The major purpose of the crop assimilation model is to provide you with an accurate prediction of crop yields. It is widely used for getting a better understanding of crop productivity, factors that might influence the yield, and the impact of climate and other factors on agriculture.
We have seen how GIS is implemented on agricultural lands to achieve precision farming. Well, regular crop monitoring provides protection from food security and scarcity problems. The data collected through geospatial technology can address all types of food security issues precisely. The crop productivity depends on the combined effects of the population, climate & weather changes, and soil loss. That is why the timely assessment of these factors is crucial to ensure maximum productivity. Now, the question is where does the geographic information system fit into all this?
Remote Sensing in Crop Assessment
The main use of remote sensing technology is in providing farmers with a proper and efficient observation of the agricultural land. Crop assimilation or modeling refers to the stimulation of the processes for agriculture. We have seen a huge demand for crop modeling in the past few years. The technology is used to ensure optimum crop growth and maximum productivity. The major purpose of implementing these methods is to guess the crop evaluation at different stages of its growth - starting from when the crop is sown to its harvest time. The model evaluates and stimulates the whole crop growth process seamlessly.
Crop Assimilation in Managing Agricultural Resources
To name a few, crop modeling involves the stimulation of photosynthesis and biomass growth. For an effective crop assimilation process, there are other parameters needed too. For example, the farmers are supposed to GIS for collecting information about the crop variety, soil condition, and management practices. This method helps you identify the crop evolution, giving you detailed insights. While the model has proven an effective solution in stimulating the parameters affecting the crop’s growth, it does not always deliver accurate data. That’s because of the uncertainty of the parametrization and changes in the climate. Plus, there are a whole lot of other factors that might make this information inadequate for crop assimilation.
As mentioned above, regular crop assimilation is pretty important as it helps you ensure sustainable crop growth and enhanced productivity. In addition to that, it helps you predict agricultural growth effectively and make necessary farming decisions that might impact your agricultural produce. The procedure is widely used for ensuring food security and better production. Most important, the geospatial data makes it easier for farmers to manage their agricultural resources in the most effective way.