Application of GIS - 2 | Agriculture Optional Notes for UPSC PDF Download

Precision farming

• Precision farming, also known as Precision Agriculture (PA) or site-specific crop management (SSCM), involves applying technology and principles to address the spatial and temporal variability present in various aspects of agricultural production. It aims to enhance long-term, site-specific and overall farm production efficiency, productivity, and profitability while minimizing unintended environmental impacts. Precision farming involves better management of agricultural inputs, such as seeds, fertilizers, pesticides, herbicides, and water, by using the right amounts of inputs at the right place and the right time. Key tools and systems required for precision farming include GPS, GIS, and RS for collecting timely geospatial information on soil, plants, and animals, which is used to gain insights and prescribe site-specific treatments to improve agricultural productivity while ensuring sustainability and environmental protection.
• The integration of GPS, GIS, and VRT (Variable Rate Technology) is a fundamental part of precision farming. VRT systems utilize information about the field, including soil maps, yield data, and information on pests, diseases, and weeds to determine the quantities of agricultural inputs (e.g., fertilizers, pesticides) and ensure their application at the right location and time, optimizing input costs. Precision farming can be divided into three stages: the Preparatory stage, Crop Growth stage, and Harvesting stage.
  GIS plays a crucial role in each of these stages:
  • Preparatory Stage: This is the planning stage that involves collecting data before planting. Data includes information about soil nutrient status, groundwater, previous crops, and their residual effects on the upcoming crops, as well as data on pests, diseases, and problematic weeds. This data is stored in a GIS system. Historical crop data from GIS helps with decisions like variable planting, determining where and to what extent to plant various crops or varieties. Soil mapping, aided by spatial interpolation in GIS, helps predict soil properties accurately, which is crucial for precision farming interventions.
  • Crop Growth Stage: During this phase, insights and data from the preparatory stage are retrieved using GIS and used to formulate and implement management practices related to irrigation, soil fertility, and protection from biotic and abiotic stressors. Advanced technologies, including remote sensing (e.g., spectral imaging), GPS, and GIS, enable precise assessments of plant characteristics, growth, health, soil conditions, and pest and disease infestations. Remote sensing data combined with GIS helps in disease control at precise locations, optimizing crop health and productivity. Data-driven recommendations are used to manage nutrients and water stress precisely, which promotes plant growth while reducing cultivation costs. Machine learning methods have been employed for the early and accurate detection of biotic and abiotic stressors.
  • Harvesting Stage: In this final phase, data collected is analyzed in GIS to generate maps and insights for future use. Yield monitoring and mapping play a critical role in precision farming. Yield monitors, when combined with GPS, provide accurate assessments of yield variability, allowing for the creation of yield maps. This information is essential for adjusting management decisions in subsequent crop seasons.

Biomass assessment

• Renewable energy sources are essential for addressing climate change and sustainability objectives. Agricultural residues, such as crop leftovers, hold great promise as a biomass-based energy source, and the global demand for them is increasing. However, a significant challenge with using agricultural residues for energy production is the variability in their availability due to seasonal and geographical differences. Effectively harnessing these residues for energy generation requires strategies to address this spatio-temporal variability, seasonal fluctuations in biomass supply, and the logistics of identifying and transporting the residues to power plants. Geographic Information Systems (GIS), combined with remote sensing, offer valuable tools for accurately identifying and assessing crop residues, planning the feedstock material for renewable energy in a given region, and optimizing the economical transportation of these residues to power plants.
• The estimation of bioenergy potential using GIS allows for technologically advanced solutions that can efficiently utilize residues from existing farming practices. This approach becomes even more beneficial as farmers transition from traditional to smart farming practices. Several efforts have utilized GIS and associated technologies for these purposes:
  • A study used the BioSTAR crop model to calculate biomass potentials for maize, triticale, and cup plant, linking them with a GIS map of the soil dataset in the Hannover region, Germany. This approach demonstrated the utility of predicting agricultural potentials under various environmental and crop management conditions.
  • In rural areas of India, rice cropland was mapped using WorldView-2 satellite images, and this map, combined with agricultural production statistics, was analyzed in GIS to assess the availability of rice straw as a feedstock for bioenergy generation. The study estimated annual rice straw availability and the potential electrical power generation, providing valuable information for energy developers and policymakers.
• To ensure the success and sustainability of biomass-based energy projects, it is crucial to consider factors such as feedstock resources, logistics, and environmental impacts. GIS and lifecycle assessment (LCA) play significant roles in this regard. LCA, particularly spatial LCA, can help evaluate the environmental impacts of bioenergy projects on various ecosystem services. The integration of LCA and GIS is recommended for conducting a holistic assessment of the environmental benefits associated with bioenergy production.
• Furthermore, an integrated GIS-based model for biomass, site optimization, and logistics cost was developed. It used indicators such as soil erosion, soil conditioning index (SCI), and crop residue yield to assess the spatial and temporal availability of crop residues and identify optimal power plant locations while considering cost factors. Prediction models based on artificial neural networks (ANNs) were developed for each of these indicators and implemented on a GIS platform. This model's utility was demonstrated in the sustainable assessment of cotton stalks for fuel pellet production, with the potential for use in assessing various types of crop residues. Additionally, models employing GIS and multi-criteria inclusion-exclusion analysis and facility location-allocation were developed to identify sustainable crop biomass on larger spatial and temporal scales and recommend suitable biogas plants, taking logistics costs into account.

Supply chain management

The use of GIS (Geographic Information System) technology has proven highly valuable in understanding and optimizing agricultural supply chains, with applications extending to various crops and locations.
Its impact on supply chain management can be categorized into three areas:

Improving Supply Chain Management Processes:

• A systematic review of recent literature has explored the role of big GIS analytics (BGA) in agricultural applications, proposing a framework for supply chains to enhance the quality of GIS applications in agriculture. This framework serves as a reference for managing big GIS data effectively and leveraging it for productivity improvement.
• Geographic Information Technologies (GITs) have been used to enhance the supply chain management process in cotton cultivation. GITs enable the visualization of current states, alternative options, and decision-making through what-if analyses for various steps in the supply chain.
• GIS has been applied to analyze supply chain patterns and describe the spatial aspects of safe crop product (SCP) in China. GIS functions such as representation, location, analysis, traceability coding, and other techniques have enabled the tracing and retracing of SCP quality in real supply chains.
• A GIS-based constrained linear programming model, optimized using the General Algebraic Modeling System (GAMS), was developed to minimize transportation and storage costs for soybean and its byproducts. This study demonstrated the utility of ArcGIS, ArcMap, and GAMS in developing optimal supply chains.

Decision Support Systems:

• For the production of biofuels from agricultural residues, a decision support system (DSS) has been developed by integrating GIS-based site selection with simulation and optimization modeling. This integrated DSS assesses cost, energy use, emissions, and minimizes supply chain costs for biofuel facility candidates.
• An intelligent spatial decision support system (ISDSS) has been proposed to overcome GIS limitations and create a knowledge base that supports decision-making. ISDSS combines GIS with intelligent systems and features spatial data mining capabilities through IoT devices.

Locating Power Plants and Developing Supply Chains:

• To ensure the sustainability of biomass-based power plants, GIS-based analysis is used to identify optimal plant locations and make informed decisions regarding supply chain development. Using open-source GIS software, suitable locations for a small-sized power plant using olive prunings as feedstock were simulated, providing supply chain cost evaluations.
• Another study integrated GIS-based analysis with optimization modeling to create a decision support system for comparing facility candidates and minimizing supply chain costs, with potential applications for similar supply chains.

Conclusions

The use of Geographic Information Systems (GIS) in agriculture has experienced significant growth in recent decades, with its applications becoming more prominent due to advancements in digital technologies. This chapter has highlighted the diverse range of applications of GIS throughout the agricultural value chain. In addition to its well-established roles in land suitability assessment, water and soil management, and addressing various agricultural stresses, GIS has increasingly found utility in new and emerging areas driven by digital agricultural tools and technologies.
These newer applications of GIS include high-resolution crop monitoring, accurate yield prediction, precision farming, and supply chain management for both agricultural produce and biomass for energy generation. GIS, with its capabilities for data collection and real-time analysis, has become instrumental in providing spatial intelligence to enhance farm productivity and profitability through precision practices. As these current and evolving applications continue to evolve, alongside existing and emerging partner technologies, GIS will remain a pivotal tool in advancing sustainable agricultural productivity.