Statistics for Information Intensive Agriculture.

Abstract

This paper proposes that productivity growth in rain fed agriculture is possible with more intensive use of available information. This may be a way out of the productivity plateau experienced since the green revolution. Some illustrative examples are discussed to spell out the ideas. This is intended more to help students of statistics understand their subject as a tool of development, rather as a ready device to be put into immediate practice.

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Preprints for Agriculture and Allied Sciences
Advisory Board
  • Leisa Armstrong, Edith Cowan University, Australia
  • Arianna Becerril García, Autonomous University of the State of Mexico, Redalyc/AmeliCA, Mexico
  • Susmita Das, Bangladesh Agricultural Research Council
  • Abeer Elhalwagi, National Gene Bank, Egypt
  • Gopinath KA, Central Research Institute for Dryland Agriculture
  • Niklaus Grünwald, USDA Agricultural Research Service
  • Sridhar Gutam, ICAR IIHR/Open Access India
  • Vinodh Ilangovan, Max Planck Institute for Biophysical Chemistry
  • Jayalakshmi M, ANGRAU, India
  • Khelif Karima, Institut National de la Recherche Agronomique d'Algérie
  • Dinesh Kumar, Indian Agricultural Statistics Research Institute
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  • Satendra Kumar Singh, Indian Council of Agricultural Research
  • Devika P. Madalli, DRTC/Indian Statistical Institute, India
  • Prateek Mahalwar, Cellulosic Technologies UG, Germany
  • Bernard Pochet, University of Liège - Gembloux Agro-Bio Tech
  • Vassilis Protonotarios, NEUROPUBLIC
  • Andy Robinson, CABI
  • Paraj Shukla, King Saud University
  • Chandni Singh, Indian Institute for Human Settlements
  • Kuldeep Singh Jadon, ICAR-Central Arid Zone Research Institute, India
  • Rajeev K Varshney, CGIAR/ICRISAT, India
  • Sumant Vyas, ICAR- National Research Centre on Camel, India
  • Oya Yildirim Rieger, Ithaka S+R/ITHAKA, USA
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