Geoinformatics, Precision Farming, and nanotechnology are examples of climate-smart, sustainable agriculture practices that utilize modern tools and techniques to optimize agricultural production, reduce input costs, and minimize environmental impact. These practices advocate for the precise application of inputs at the appropriate time and place, considering soil and crop variability. The development of spectral and hyperspectral sensors, as well as high-resolution images obtained through satellite technology, has accelerated information generation and collection in these areas.

Innovations in nanotechnology, such as nanoscale biosensors and nanoparticles, have also shown promise in enhancing agricultural productivity by improving the use of fertilizers, pesticides, and other inputs. The goal of these practices is to achieve sustainable agriculture under declining resources.

The book covers all the above to cater to the needs of any beginner in the above fields

Dr Manpreet Kour is Doctorate on simulation modeling in Agronomy, presently working as Junior Scientist (Senior scale) AICRP on Weed Management, Division of Agronomy, SKUAST Jammu, with a teaching, research, and extension experience of about 17 years. She has teaching experience of more than 150 credit hours and handled seven research projects. Her publications include 33 research papers, 50 abstracts, and 68 extension publications along with eight manuals, eight popular articles, 37 chapters in book/compendium, two research bulletins, and presented more than 22 papers in various seminars and symposia. Apart from these 10 recommendations and technologies have been developed. Her contribution has been recognized by 16 awards/appreciations.

Dr Vikas Sharma is Professor and Head, Division of Soil Science and Agricultural Chemistry at the Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, India. He obtained his doctorate from the Punjab Agricultural University, Ludhiana, India and did postdoc under the Raman Fellowship from West Virginia University, USA. He has been a Visiting Professor at the Auburn University, USA. He is the founder Head of Centre for Organic and Natural Farming and has been instrumental in establishing the center from scratch. His research interests include digital soil mapping, pedometrics, soil carbon, soil conservation, and land degradation. Most of his work pertains to soils in Indian Himalayas. He has worked on soil erosion risk mitigation, carbon pools, and carbon losses from the Himalayan soils and nutrient dynamics in the erosion-prone foothill of Himalayas. He has utilized his expertise in remote-sensing and GIS in assessing and mapping soil resources in the region. He is presently focusing on utilizing machine learning models in soil mapping. He is also involved in teaching and advising students’ research. He has executed multiple projects and presently is the PI of the prestigious NAHEP project at SKUAST Jammu. He has over 90 research articles and 30 book chapters to his credit. His latest authored book is on Soils in the Hindukush Himalayas has been published by Springer.

Dr Harsimran Singh Bindra is Doctorate in nanoscience, presently working as Assistant Professor (Contractual) Division of Biotechnology, SKUAST Jammu and have teaching, research and extension experience of about 8 years. His publications include 12 research papers, eight popular articles, four chapters in book/compendium, and presented more than four papers in various seminars and symposia. Apart from these he owns one patent.

01. Geographical information system 02. Global positioning system 03. Remote Sensing 04. Generation of spectral profiles of different objects 05. Thematic layers 06. Crop differentiation, yield monitoring and soil mapping 07. Precision Agriculture 08. Site specific nutrient management (SSNM) 09. Fertilizer recommendation based on VRT (variable rate technology) 10. Soil Crop Test Response (STCR) 11. Project formulation related to precision farming. 12. Nano-particles 13. Nano-fertilizers 14. Nano pesticides 15. Simulation model