Application of GIS - 1 | Agriculture Optional Notes for UPSC PDF Download

GIS applications

The advent of digital agriculture, often regarded as the fourth agricultural revolution, has completely revolutionized farming practices. This transformation is primarily attributed to advancements in geospatial technologies, sensors, artificial intelligence, robotics, and various other tools and technologies. The key to this transformation lies in the precise identification of issues within cropland and the comprehensive monitoring and management of all stages throughout the agricultural value chain.

This process necessitates both image and non-image data, combined with spatial context. Geographic Information Systems (GIS), equipped with its component tools and analytical modules, in tandem with data collected through complementary technologies like remote sensing and GPS, offers a clear and intuitive way to visualize information. This visual representation is instrumental in data-driven decision-making aimed at enhancing crop productivity.

Although GIS has been applied in agriculture for some time, the number of applications has surged in recent years due to technological advancements.
This has led to the emergence of numerous common and innovative agricultural applications, which are elaborated upon below:

Land suitability assessment and land use planning

• In our current era, we face the formidable challenge of feeding a growing global population while the availability of fertile land is diminishing. To address this challenge, we must optimize the use of natural resources to maximize their benefits. Geographic Information Systems (GIS) serve as an excellent platform for assessing land quality for various applications. Among researchers, the most widely preferred approach for land use planning involves Multi-criteria Decision-Making (MCDM) methods integrated with GIS. This approach leverages various GIS features, including data related to soil type distribution, soil texture maps, groundwater levels, soil fertility, soil pollution, hydraulic soil conductivity (Ks), slope (S), soil texture (ST), depth to the water table (DTW), electrical conductivity of groundwater (ECw), climate conditions, topography, satellite data, and identifies interactions, dependencies, and their combined impact on sustainable land use.
• For example, Chen et al. examined the sensitivity of MCDM models for land suitability assessment in irrigated agriculture, emphasizing the importance of weight sensitivity in input features. Zolekar and Bhagat utilized a GIS-based MCDM model with satellite images for evaluating agricultural practices in hilly regions. Pan and Pan employed a two-step Analytic Hierarchy Process (AHP) for GIS-based crop suitability assessment, focusing on the selection of relevant evaluation factors. They recommended proper land use based on land suitability maps. Another study focused on the growth requirements of wheat crops, using an Analytic Network Process (ANP) model to assess interdependence among input features for site suitability evaluation of citrus crops. AHP integrated with geostatistics proved valuable for maize cultivation land suitability mapping in calcareous and saline-sodic soils, facilitating land reclamation planning with appropriate conservation practices. An integrated fuzzy membership and GIS model was used to analyze arable land suitability for farming, offering an approach that considered partial memberships and revealed better suitability for groundnut cultivation compared to traditional finger millet cultivation.
• In light of the declining availability of land and natural resources and the rapidly increasing demand for food production, the integration of fuzzy logic, AHP, and GIS is becoming crucial for generating precise land suitability maps. The combination of these techniques helps overcome the uncertainties, subjectivities, and hierarchical characteristics associated with traditional land suitability assessments. GIS serves as a powerful tool for delineating study areas, managing geographic data, processing maps, and presenting results in land suitability assessments. Researchers are also exploring the integration of artificial intelligence with GIS for more efficient land use planning.

Water resource management

• The abundant supply of water is a fundamental requirement for meeting the increasing demand for food production by the growing global population. Farmers bear the responsibility of providing food for an estimated 10 billion people by 2050, which necessitates a 50% increase in food production compared to 2013 levels. However, the availability of clean water is decreasing, and relying solely on rainfall is no longer a viable option for farmers. In this challenging scenario, effective water resource management becomes the key to success.
• Irrigation is recognized as the primary solution to fulfill the water requirements in agriculture. The integration of GIS technology, coupled with remote sensing, has already demonstrated its effectiveness in the management of water resources. Researchers have emphasized that remote sensing can complement traditional geophysical models for assessing groundwater potential and recharge experiments. Moreover, the potential of GIS for groundwater management has received significant support from researchers.
• Several studies have integrated groundwater models like MODFLOW with GIS for tasks such as watershed prioritization. Combining GIS and remote sensing, researchers have delineated groundwater potential zones based on lineament and hydro-geomorphological maps generated from remote sensing images. These delineated zones have been found to correlate with well-yield data.
• Furthermore, the combination of GIS and remote sensing data has proven valuable for assessing sub-watershed level runoff and sediment yield, reducing data processing time and producing accurate results. One of the most common applications of GIS is determining the suitability of irrigation for a specific geographical area. For instance, in a water-scarce region like the UAE, a study considered non-renewable sources such as desalination and treated sewage effluent for assessing irrigation suitability. This analysis incorporated land management, topography, climate conditions, soil capabilities, and water potential into an Analytical Hierarchical Process (AHP) GIS model to evaluate crop suitability. The results indicated that the land was unsuitable for cereals and vegetables but suitable for sorghum, jojoba, fruits, date palm, and forage cultivation.
• As clean water resources decline, researchers and policymakers are exploring alternatives for irrigation water. One study assessed the feasibility of using urban treated wastewater as an irrigation source. The study employed the Multi-Criteria Decision-Making (MCDM) method within a GIS environment, utilizing the Analytic Hierarchy Process (AHP). This analysis considered factors such as crop cultivation suitability, nitrate contamination burden, and aquifer vulnerability to determine the suitability of treated wastewater.
• Additionally, an irrigation scheduling model, ISAREG, was integrated with GIS to provide efficient irrigation scheduling advice and identify water-saving practices. This approach proved successful for scheduling irrigation and conserving water during both wet and dry years. However, it was noted that while intensifying irrigation is beneficial for food production, it can lead to soil salinization and waterlogging. Therefore, a strong emphasis should be placed on using GIS and remote sensing technology to monitor problem areas and plan conservation and preventive measures.

Soil health and fertility management

• The health and fertility of soil directly influence agricultural productivity by controlling the availability of nutrients and water to crops. Over time, soil fertility has been deteriorating due to various factors such as pollution, sealing, overgrazing, waterlogging, excessive use of agricultural chemicals, and erosion. Therefore, it is essential to assess soil health and fertility status to plan effective practices for site-specific management or precision farming.
• Soil macronutrients (N, P, and K), micronutrients (Zn, Mn, and Fe), pH levels, soil organic carbon (SOC), water retention capacity, erosion status, and moisture content are key indicators used to assess soil fertility status. Geospatial analysis techniques, such as spatial interpolation, Multi-Criteria Decision Analysis (MCDA), and Ordered Weighted Averaging (OWA), are commonly employed to provide spatiotemporal insights into soil health and fertility status.
• Evaluating soil erosion status is crucial for assessing soil quality and planning for agriculture. Geospatial maps of soil erodibility, generated using methods like Inverse Distance Weighted (IDW), are valuable tools for sub-watershed level land use planning.
• The integration of remote sensing and GIS technology has been used to assess soil fertility status. These studies utilize satellite images for land use classification, the Revised Universal Soil Loss Equation (RUSLE) for soil erosion estimation, and geostatistical models to identify spatial variations in soil erosion and nutrient availability. Additionally, methods like IDW and OWA are used to create soil nutrient maps, which are further processed using fuzzy inference systems to generate soil fertility maps.
• The relationship between crop productivity and soil fertility is evident, and GIS-based soil maps provide valuable information about field-specific crop suitability. One example of GIS-enabled technology is a cloud-based decision support system for soil fertility management, which offers fertilizer recommendations based on soil tests and crop responses. GPS- and GIS-based soil fertility maps are also used for comprehensive soil health monitoring.
• These geospatial soil maps are employed to make informed decisions about soil health and nutrient management. For instance, they can recommend the application of specific materials, such as paper mill sludge, to address soil acidity and promote the cultivation of certain crops. The integration of remotely sensed data, fine-to-coarse spatiotemporal resolutions, and modeling techniques has made it possible to develop accurate soil fertility models.
• Studies have also used weighted space fuzzy clustering in combination with soil nutrient space mutation distribution to characterize soil fertility. This information helps optimize fertilizer recommendation systems. To prevent the degradation and loss of prime farmlands, it is essential to employ best management practices, compatible land use and cover changes, and land suitability analysis. These practices can include soil erosion management, soil biodiversity improvement, and rehabilitative farming systems, all aimed at enhancing soil quality and crop yields.
• Fertility maps generated through fuzzy evaluation methods have shown that total nitrogen and soil organic matter are higher in paddy fields. These maps also provide insights into suitable soil qualities under different land use and climatic conditions. At the sub-watershed level, nutrient mapping has indicated that available N, P, S, Zn, and Fe play crucial roles in soil fertility. As a result, fertility maps and their relationships with soil properties and crop yields serve as valuable information systems for precision agriculture.

Biotic and abiotic damage assessment and intervention

• Crop damage caused by biotic factors like insects, fungi, and pests can result in significant yield losses, ranging from 15% to 70%. These losses not only affect the agricultural supply chain but also impact the livelihoods of farmers and the overall economy. Changing weather patterns have made crops more vulnerable to pest infestations and diseases. While crop protection methods are valuable for addressing crop health, the lack of timely information about pests and diseases can lead to irrepressible damage.
• GIS technology offers significant potential for site-specific pest and disease management. Remote sensing and GIS-based early warning systems are particularly beneficial for farmers, allowing them to take timely control measures to reduce production costs. In addition to early warning systems, pest population density maps are instrumental in identifying hotspots and providing advice to farmers. For example, information about the geospatial density of pests like the oriental fruit moth can reduce crop injury and pest populations through geographically suitable management measures.
• The impact of climate change on pest attack patterns is evident when comparing the current and predicted geospatial distribution of pest species. Pest distribution maps enable farmers, agricultural experts, and policymakers to develop strategies for pest management in the future. Tracking the migratory patterns of pests is crucial, given instances of sudden outbreaks of pests in new geographic locations.
• Remote sensing and GIS also play a crucial role in monitoring the habitats of pest species and assessing the extent of crop damage caused by pests and diseases. These technologies are cost-effective and rapid for damage assessment, making them valuable tools in agriculture. Researchers have demonstrated the feasibility of detecting pest and disease types and mapping their severity using remote sensing images. These damage assessment maps contain essential spatial information about the extent of crop damage over multiple years and across different regions. They can serve as aids for insurance settlements and government subsidies for farmers.
• Natural calamities, such as floods, cause irreversible damage to agriculture. Rapid mapping and quantification of this damage are essential for economic recovery and decision-making. Geospatial models are used to assess the impact of flood events on agricultural production. Water surfaces are generated through interpolation of flood depth marks and digital elevation models (DEMs) to create flood inundation maps. These maps, when overlaid with land use data, provide accurate estimates of the agricultural areas affected by floods.
• Drought is another significant constraint to agricultural productivity. Understanding drought hotspots and climatology is essential to minimizing its impact. Using satellite-derived data like the Normalized Difference Vegetation Index (NDVI) from MODIS, GIS-based characterizations of climate variability and drought zones offer opportunities for adopting strategic measures to maximize productivity.

Crop monitoring and yield prediction

• Crop monitoring and yield prediction are essential for assessing economic returns and ensuring food production for food security. Traditional methods of crop yield estimation can be inaccurate and require extensive data collection. Remote sensing (RS), GPS, and GIS technologies offer advantages in assessing temporal and spatial crop dynamics and predicting yields.
• Remote sensing and GIS, when combined with input from other technologies, provide efficient solutions for monitoring crop health and developing yield prediction models at various spatial scales. RS data acquired by satellites, aircraft, or unmanned aerial vehicles (UAVs) offer insights into crop characteristics, soil conditions, and overall crop health. These data help assess factors like crop vigor, disease or pest infestations, and responses to abiotic stresses, such as drought. Geospatial data, collected over time, assist in monitoring crop health changes and permit management interventions. Vegetation indices derived from RS data, such as NDVI (Normalized Difference Vegetation Index), play a crucial role in assessing crop health and predicting yield.
• Crop health assessment is particularly vital for smallholder farms, as their subsistence depends on crop productivity. Leveraging data from UAVs has proven crucial for smallholders to take corrective measures promptly. Multispectral UAV imagery and machine learning algorithms can help estimate chlorophyll content in crops and create maps showing spatial heterogeneity. Small farms, although occupying a smaller agricultural area, significantly contribute to food production.
• Accurate yield prediction is essential for implementing location-specific management practices and interventions. RS technologies, in conjunction with GIS, have been used to predict crop yields. Linear regression-based yield prediction models, using NDVI values at various growth stages, can help farmers implement changes to improve productivity. Time-series data from satellites and GIS have been employed to develop operational models for yield forecasting, providing early intervention opportunities. Vegetation indices derived from satellite images have been highly effective in predicting yields, creating productivity zone maps for better management practices.
• In addition to vegetation indices and GIS, some researchers have combined GIS with crop simulation or physiological models for yield prediction. Crop simulation models help assess the impact of climate variations on crop performance, while physiological crop models measure the impact of climate changes on productivity. Combining RS, GIS, and crop models can offer precise yield estimates, particularly in areas prone to abiotic stresses like drought. Decision support systems have also been created based on simulation models and GIS for agronomic decisions. Integrating models with GIS expands their application to regional or global levels and can help determine potential crop combinations under specific growing conditions. GIS-based crop simulations have demonstrated the effects of global climate change on future yields, emphasizing the need for climate-resilient crop varieties.
• Some applications involve assessing damage in high-value crops like cranberries, which exhibit yield variations due to soil characteristics affecting water and nutrient availability. GIS, GPS, and RS have been used to create spatial variation maps, enabling the analysis of crop losses within different zones of a field or across the entire field.