Geo-informatics in Agricultural Research and Development: An IASRI Perspective


Geo-informatics is integrated technology for collection, transformation and generation of information from integrated spatial and non-spatial data bases. Remote sensing, Geographical Information Sciences (GIS), Global Positioning Systems (GPS), Relational Data Base Management Systems (RDBMS) are some of its important ingredients. It is a powerful tool for assessment, monitoring, planning and management of agricultural research and development. Management of agricultural resources is a myriad activity of conservation practices and land/water resources aimed at increasing the food production. Substantial increase in crop production could be achieved by bringing additional land under cultivation, improved crop management technology through use of high yielding, input responsive and stress tolerant crop varieties, improved pest control as well as by increasing irrigation and fertilizer inputs. These inputs together with reliable information on i) existing land use and acreage under various crops, ii) soil types and extent of problem soils, iii) monitoring of surface water bodies (to determine water availability in irrigation systems) for ground water development and (iv) management of natural calamities etc. will enable formulation of appropriate strategies to sustain the pace of agricultural development. This in turn calls for a holistic approach, which must combine short-term management of agricultural resources at micro-level with long term global perspectives, keeping in view of socio-economic and cultural environment of the people. The role of space geo-informatics in finding new resources for agriculture development for optimally managing the already available resources in order to maximize agriculture production is recognized world wide and is found to be highly potential. Agricultural remote sensing involving crops and soils are quite complex. These complexities are due to dynamic nature and inherent complexity of biological materials. In order to handle these complex problems, remote sensing technology offers numerous advantages over traditional methods of conducting agricultural and other resource surveys. Advantages include, the potential for accelerated surveys, capability to achieve a synoptic view under relatively uniform lighting conditions, availability of multi- spectral data for providing intense information, capability of repetitive coverage to depict seasonal and long-term changes and availability of imagery with minimum distortion etc. Therefore, it permits direct measurement of important agro-physical parameters. Remote sensing of earth resources utilizes electromagnetic waves, which ranges from short wave length ultra violet through visible near infrared and thermal infrared in the longer wave length, active radar and passive microwave systems. A great advancement in applications of computers to this science is the development of capability of storing vast and varied information, ranging from historical information and aerial photography to spacecraft data, ground reference, and other forms of ancillary data. All these information is stored in the form of highly useful database/information system. Thus remotely sensed data and its derived information have become an integral component of agricultural management system in the country. Applications of space borne remote sensing data for large area crop survey was explored in USA under Corn Blight Watch Experiment (CBWE) in 1971 which was followed by large number of

Download full-text


Available from: Anil Rai, Oct 05, 2015
29 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: Crop yield is mainly dependent on weather, soil and technological inputs. Yield forecasting models have been developed mainly using multiple regression techniques based on biometrical characters of the plants and/or weather parameters. Matiset al. (1985) proposed another approach of crop yield modelling using Markov Chain theory based on biometrical characters. The integration of remote sensing with other technologies has provided an immense scope to improve upon the existing crop yield models. In the present study, multi date spectral data during crop growth period was used in Markov Chain Model to forecast wheat yield. The results indicate that the use of spectral data near the maximum vegetative growth of wheat crop improves the efficiency and reliability of yield forecast about a month before its actual harvest.
    Journal of the Indian Society of Remote Sensing 08/1996; 24(3):145-152. DOI:10.1007/BF03007327 · 0.76 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: General crop yield estimation surveys are conducted throughout the country for estimating crop yield of all major crops. In an earlier study satellite spectral data has been used along with survey data to develop a more efficient post-stratified estimator of crop yield which suggested that with the use of satellite data along with crop yield data, it is possible to develop small area estimates of crop yield at tehsil/block level with the existing sampling design. In the present study small area estimates at tehsil (Subadministrative Unit) level for the Rohtak district, Haryana, India, for the period 1995-1996 were developed using crop yield data based on crop cutting experiments and satellite spectral data from the Indian Remote Sensing Satellite IRS-1B-LISS-II for 17 February 1996.
    International Journal of Remote Sensing 01/2002; 23(1):49-56. DOI:10.1080/01431160010014756 · 1.65 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Agroforestry helps in providing all necessities of life while at the same time maintains the quality of land resources. In this paper, an attempt has been made to develop a suitable model for prediction of area under agroforestry. The study has been conducted in Yamunanagar district of Haryana state and important factors responsible for growth of agroforestry in this district have been identified based on a household survey. Area under agroforestry for non-sampled villages of the district has been predicted using the developed model. A map depicting percentage area under agroforestry for all the villages of the district, classified into three different categories, has been generated using Geographic Information System (GIS) software.
    Indian Journal of Agricultural Sciences 01/2007; 77(1). · 0.14 Impact Factor