Increasing interest in Remote Sensing and advancement in the GIS technology has open gateways to various researches—agriculture being one of them. The easy viability of hyperspectral images (thanks to USGS EROS Center) and the enthusiasm of the scientists in using their expertise interpreting and analyzing these images to produce robust, sensible, and understandable products has not only benefited the scientific community but also the local stakeholders, decision-makers, and mankind as a whole. The role of GIS has always stood out in this term.
Application of GIS in agricultural is a huge term. So, let’s break this into small chunks and see how has GIS impacted in agriculture?
1. Precision Agriculture
Precision agriculture is a farming management concept that uses new technologies like GIS, GPS, and remote sensing to increase crop yields and profitability while lowering the levels of traditional inputs needed to grow crops (land, water, fertilizer, herbicides, and insecticides). In other words, a controlled way of farming where farmers can decide what crops to plant, what nutrient (and what amount) to use, and when to farm based on the models and algorithms developed using advanced technologies like GPS and GIS tools.
(Mulla & Miao, 2016) stated that “Precision farming has always relied on technology for data collection and analysis at specific locations and times across agricultural fields. The earliest technology was GIS, followed by GPS, and remote sensing”.
Given the ability to compile and analyze data in real-time, farmers can make wise decisions while farming. Besides that, the use of GIS in precision farming could have the following advantages:
- Crop nitrogen stress detection
- Crop acreage estimation
- Crop population estimation
- Crop yield prediction
- Grain quality assessment
- Crop mapping
- Insects, Diseases, and weed detection
- Soil nutrient determination
- Water supply and management
Due to the advancement in remote sensing and added functionalities in GIS, the characterization, modeling, and mapping of almost any crops have been possible—which is to say, the future of precision agriculture heavily relies upon GIS and Remote Sensing.
2. Land Use Land Cover Mapping
Land use and land cover (LULC) mapping and land cover change (LCC) mapping have grown as one of the prime topics in land resource monitoring and modeling. Whether that be agricultural land-use change or vegetation cover change, it always gives valuable information on how the earth’s surface is being used.
The thematic mapping of the land cover, modeling the soil samples, hydrology modeling, determining the slopes and aspects, weed mapping, land cover change trends mapping, and so on are some of the aspects that have become easier and robust using GIS.
3. Land use policies
It is very crucial to make the right decision when it comes to using the land. Human involvement with the environment, particularly regarding using natural resources, has proven to become detrimental to the environment. For the sake of agriculture human
4. Best Management Practices
Best management practices (BMPs) are methods that not only improve the crop yield production but also prevent or reduce non-point source pollution to help achieve water quality goals.
The capability of GIS in obtaining the information on soil property detection, weed detection, herbicide drifting sensing, N stress detection, pest and disease detection, and crop herbicide damage detection (Haibo, et al., 2019) has helped in varieties of ways to determine the best management practices.
These are some of the applications. There are many other applications of GIS. We will talking about them in another blogs!
- Haibo, Y., Yanbo, H., Tanf, L., Tain, L., Dhatnagar, D., & Cleveland, T. E. (2019). Using Hyperspectral Data in Precision Farming Applications. In T. S. Prasad, J. G. Lyon, & A. Huete, Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation (pp. 3-36). CRC Press.
- Mulla, J. D., & Miao, Y. (2016). Precision Farming. In P. S. Thenkabail, Land Resource Monitoring, Modeling, and Mapping with Remote Sensing (pp. 161-178). CRC Press.