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Space inputs for precision agriculture: scope for proto-type experiments in the diverse Indian agro-ecosystems

Case Studies
Most of the case studies, as reported, are mainly from US and European countries representing the agricultural systems, quite different from that of the India's typical agricultural scenario. Unless few success stories are produced taking into account the different farming practices and cropping systems available in India, Precision Agriculture practices cannot be replicated.

Models of Precision Agriculture drawn from US & UK A framework of the precision agriculture system, being followed in US & UK, is depicted in Figure 2.1. At the core of the system is a GIS database, which are knowledge based and form the part of decision-making. The GIS databases include the following layers: field topography, soil types, surface drainage, sub-surface drainage, soil testing results, rainfall, irrigation, actual chemical application rates, and or even more frequently. The GIS enables a study of the relationship between these layers of information to determine cause and effect and to base decisions upon this knowledge.

Fig 2.1. Precision Farming Overview

The main components, which make up a variable rate application system, are shown in Figure 2.2. Not all systems will necessarily contain all of the components shown. As variable rate technology develops, other system components may be included. The central component of variable rate application equipment is the computer/controller. This device receives information from several sources, which will in turn be used to control the application equipment. The controller may receive information from the application equipment and other sensors to maintain a database on the actual application rate as a function of field position

Each field operations are governed by VRT systems. Tillage depth varies according to field location; for example, sub-soiling depth is dependent on field location. Seeding rates varies according to field location, which depends on factors such as topography and soil type. Fertilizer application rates vary in relationship to factors such as soil type and the results from either real time or pre-application testing. Application of insecticides is dependent on insect location from either scouting reports or from aerial imaging. In like manner, the application of all inputs to the crop production process varies with field location.

Fig 2.2: Components of VTR systems

There are two methodologies for implementing precision, or site-specific, farming. Each method has unique benefits and can even be used in a complementary, or combined, fashion:

The first method, Map-based, includes the following steps: grid sampling a field, performing laboratory analyses of the soil samples, generating a site-specific map of the properties and finally using this map to control a variable-rate applicator. During the sampling and application steps, a positioning system, usually DGPS (Differential Global Positioning System) is used to identify the current location in the field.

The second method, Sensor-based, utilizes real-time sensors and feedback control to measure the desired properties on-the-go, usually soil properties or crop characteristics, and immediately use this signal to control the variable-rate applicator

Remote Sensing technologies are used for in-season crop condition assessment including the crop moisture or nutrient stress and other conditions--indicating the need for irrigation and fertilizers or insecticides. All of these data give farmers more opportunities to tailor their management decisions to their farm's needs. These inputs help the farmers to locate and analyze the stressed part of the field with reduced sampling in map-based technique.

Space Applications to Agriculture in India: A Brief Profile
Agriculture has been at the top of our priorities for space applications. The road map of agricultural applications started with the first remote sensing experiment on identification of coconut root-wilt diseases in Kerala using Infrared aerial photography- way back in 1969-70. Since then, space applications to agriculture sector have touched almost all the segments of agricultural ecosystems. These include the mapping and monitoring of major crops, soils/degraded lands, command areas, wastelands, surface & ground water, floods & drought, and watersheds.

Agricultural statistics that provides the vital informatics base to agriculture sector originates from age-old village level Patwari system. It often moves upward with its inherent subjectivity and bias. There are hundreds of crucial decisions at different levels, which are taken purely based on this. In order to strengthen the foundations of agricultural statistics in the country, a remote sensing based the nationwide mission called pre-harvest Crop Acreage and Production Estimation (CAPE) was launched in late 80s. Covering all the major cereals, pulses and oilseeds, CAPE provides in-season crop statistics with 90/90 accuracy at state level. The CAPE could thus provide the scientific basis of agricultural statistics and is transitioning further to yet another institutional destination entitled Forecasting Agricultural Output using Space Agro-meteorology and Land-based Observations (FASAL) within Ministry of Agriculture itself. Synthesizing the state-of-the-art in econometrics, agricultural meteorology and remote sensing based modeling, FASAL envisages the multiple productions forecasting of the major crops with improved accuracy to the extent of 95/95 criteria, timeliness and scope in terms of covering the whole country. The FASAL could be used to provide scientific solutions facilitating crop insurance, bridging the gaps between crop production and post-harvest technology, pricing and policy decisions.

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