Current Science

K. K. Singh ¹, A. Kumar ², A. V. Kulkarni ³, P. Datt ¹, S. K. Dewali ¹, V. Kumar ¹, R. Chauhan ¹

Affiliations
1 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
2 National Institute of Technology, Kurukshetra 136 119, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN

Abstract

Snow depth is an important parameter for avalanche forecast and hydrological studies. In the Himalaya, manual snow depth data collection is difficult due to remote and rugged terrain and the severe weather conditions. However, microwave-based sensors in various satellites have the capability to estimate snow depth in all weather conditions. In the present study, experiments were performed to establish an algorithm for snow depth estimation using ground-based passive microwave radiometer with 6.9, 18.7 and 37 GHz antenna frequencies at Dhundhi and Patseo, Himachal Pradesh, India. Different layers in the snowpack were identified and layer properties, i.e. thickness, density, moisture content, etc. were measured manually and using a snow fork. Brightness temperature (T_B) of the entire snowpack and of the individual snow layers was measured using passive microwave radiometer. It was observed that T_B of the snow is affected by various snow properties such as depth, density, physical temperature and wetness. A decrease in T_B with increase in snow depth was observed for all types of snow. T_B of the snowpack was observed higher at Dhundhi in comparison to Patseo. Based on the measured radiometer data, snow depth algorithms were developed for the Greater Himalaya and Pir-Panjal ranges. These algorithms were validated with ground measurements for snow depth at different observatory locations and a good agreement between the two was observed (absolute error: 7 to 39 cm; correlation: 0.95).

Keywords

Brightness Temperature, Microwave Radiometer, Snow Depth Algorithm, Snowpack.

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T. Bhattacharyya ¹, D. Sarkar ¹, S. K. Ray ¹, P. Chandran ¹, D. K. Pal ², D. K. Mandal ¹, J. Prasad ¹, G. S. Sidhu ³, K. M. Nair ⁴, A. K. Sahoo ⁵, T. H. Das ⁵, R. S. Singh ⁶, C. Mandal ¹, R. Srivastava ¹, T. K. Sen ¹, S. Chatterji ¹, N. G. Patil ¹, G. P. Obireddy ¹, S. K. Mahapatra ³, K. S. Anil Kumar ⁴, K. Das ⁵, A. K. Singh ⁶, S. K. Reza ⁷, D. Dutta ⁵, S. Srinivas ⁴, P. Tiwary ¹, K. Karthikeyan ¹, M. V. Venugopalan ⁸, K. Velmourougane ⁸, A. Srivastava ⁹, Mausumi Raychaudhuri ¹⁰, D. K. Kundu ¹⁰, K. G. Mandal ¹⁰, G. Kar ¹⁰, S. L. Durge ¹, G. K. Kamble ¹, M. S. Gaikwad ¹, A. M. Nimkar ¹, S. V. Bobade ¹, S. G. Anantwar ¹, S. Patil ¹, V. T. Sahu ¹, K. M. Gaikwad ¹, H. Bhondwe ¹, S. S. Dohtre ¹, S. Gharami ¹, S. G. Khapekar ¹, A. Koyal ⁴, Sujatha ⁴, B. M. N. Reddy ⁴, P. Sreekumar ⁴, D. P. Dutta ⁷, L. Gogoi ⁷, V. N. Parhad ¹, A. S. Halder ⁵, R. Basu ⁵, R. Singh ⁶, B. L. Jat ⁶, D. L. Oad ⁶, N. R. Ola ⁶, K. Wadhai ¹, M. Lokhande ¹, V. T. Dongare ¹, A. Hukare ¹, N. Bansod ¹, A. Kolhe ¹, J. Khuspure ¹, H. Kuchankar ¹, D. Balbuddhe ¹, S. Sheikh ¹, B. P. Sunitha ⁴, B. Mohanty ³, D. Hazarika ⁷, S. Majumdar ⁵, R. S. Garhwal ⁶, A. Sahu ⁸, S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, A. Kumar ⁹, N. Gautam ¹, B. A. Telpande ¹, A. M. Nimje ¹, C. Likhar ¹, S. Thakre ¹, A. P. Nagar ¹

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
3 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
8 Central Institute for Cotton Research, Nagpur 440 010, IN
9 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
10 Directorate of Water Management, Bhubaneswar 751 023, IN

Abstract

Land-use planning is a decision-making process that facilitates the allocation of land to different uses that provide optimal and sustainable benefit. As land-use is shaped by society-nature interaction, in land-use planning different components/facets play a significant role involving soil, water, climate, animal (ruminant/ non-ruminant) and others, including forestry and the environment needed for survival of mankind. At times these components are moderated by human interference. Thus land-use planning being a dynamic phenomenon is not guided by a single factor, but by a complex system working simultaneously,which largely affects the sustainability. To address such issues a National Agricultural Innovation Project (NAIP) on 'Georeferenced soil information system for land-use planning and monitoring soil and land quality for agriculture' was undertaken to develop threshold values of land quality parameters for land-use planning through quantitative land evaluation and crop modelling for dominant cropping systems in major agro-ecological sub-regions (AESRs) representing rice-wheat cropping system in the Indo-Gangetic Plains (IGP) and deep-ischolar_mained crops in the black soil regions (BSR). To assess the impact of landuse change, threshold land quality indicator values are used. A modified AESR map for agricultural landuse planning is generated for effective land-use planning.

Keywords

Agriculture, Georeferenced Soil Information System, Land-Use Planning, Spatial Database.

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P. Chandran ¹, P. Tiwary ¹, T. Bhattacharyya ¹, C. Mandal ¹, J. Prasad ¹, S. K. Ray ¹, D. Sarkar ¹, D. K. Pal ², D. K. Mandal ¹, G. S. Sidhu ³, K. M. Nair ⁴, A. K. Sahoo ⁵, T. H. Das ⁵, R. S. Singh ⁶, R. Srivastava ¹, T. K. Sen ¹, S. Chatterji ¹, N. G. Patil ¹, G. P. Obireddy ¹, S. K. Mahapatra ³, K. S. Anil Kumar ⁴, K. Das ⁵, A. K. Singh ⁶, S. K. Reza ⁷, D. Dutta ⁵, S. Srinivas ⁴, K. Karthikeyan ¹, M. V. Venugopalan ⁸, K. Velmourougane ⁸, A. Srivastava ⁹, Mausumi Raychaudhuri ¹⁰, D. K. Kundu ¹⁰, K. G. Mandal ¹⁰, G. Kar ¹⁰, J. A. Dijkshoorn ¹¹, N. H. Batjes ¹¹, P. S. Bindraban ¹¹, S. L. Durge ¹, G. K. Kamble ¹, M. S. Gaikwad ¹, A. M. Nimkar ¹, S. V. Bobade ¹, S. G. Anantwar ¹, S. V. Patil ¹, K. M. Gaikwad ¹, V. T. Sahu ¹, H. Bhondwe ¹, S. S. Dohtre ¹, S. Gharami ¹, S. G. Khapekar ¹, A. Koyal ⁴, K. Sujatha ⁴, B. M. N. Reddy ⁴, P. Sreekumar ⁴, D. P. Dutta ⁷, L. Gogoi ⁷, V. N. Parhad ¹, A. S. Halder ⁵, R. Basu ⁵, R. Singh ⁶, B. L. Jat ⁶, D. L. Oad ⁶, N. R. Ola ⁶, K. Wadhai ¹, M. Lokhande ¹, V. T. Dongare ¹, A. Hukare ¹, N. Bansod ¹, A. H. Kolhe ¹, J. Khuspure ¹, H. Kuchankar ¹, D. Balbuddhe ¹, S. Sheikh ¹, B. P. Sunitha ⁴, B. Mohanty ³, D. Hazarika ⁷, S. Majumdar ⁵, R. S. Garhwal ⁶, A. Sahu ⁸, S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, A. Kumar ⁹, N. Gautam ¹, B. A. Telpande ¹, A. M. Nimje ¹, C. Likhar ¹, S. Thakre ¹

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
3 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi, 110 012, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
8 Central Institute for Cotton Research, Nagpur 440 010, IN
9 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
10 Directorate of Water Management, Bhubaneswar 751 023, IN
11 ISRIC, Wageningen, NL

Abstract

Soil information system in SOTER (soil and terrain digital database) framework is developed for the Indo- Gangetic Plains (IGP) and black soil regions (BSR) of India with the help of information from 842 georeferenced soil profiles including morphological, physical and chemical properties of soils in addition to the site characteristics and climatic information. The database has information from 82 climatic stations that can be linked with the other datasets. The information from this organized database can be easily retrieved for use and is compatible with the global database. The database can be updated with recent and relevant data as and when they are available. The database has many applications such as inputs for refinement of agroecological regions and sub-regions, studies on carbon sequestration, land evaluation and land (crop) planning, soil erosion, soil quality, carbon and crop modelling and other climate change related research. This warehouse of information in a structured framework can be used as a data bank for posterity.

Keywords

Black Soil Region, Database, Indo-Gangetic Plains, SOTER.

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T. Bhattacharyya ¹, D. Sarkar ¹, S. K. Ray ¹, P. Chandran ¹, D. K. Pal ¹, D. K. Mandal ¹, J. Prasad ¹, G. S. Sidhu ², K. M. Nair ³, A. K. Sahoo ⁴, T. H. Das ⁴, R. S. Singh ⁵, C. Mandal ¹, R. Srivastava ¹, T. K. Sen ¹, S. Chatterji ¹, N. G. Patil ¹, G. P. Obireddy ¹, S. K. Mahapatra ², K. S. Anil Kumar ³, K. Das ⁴, A. K. Singh ⁵, S. K. Reza ⁶, D. Dutta ⁷, S. Srinivas ³, P. Tiwary ¹, K. Karthikeyan ¹, M. V. Venugopalan ⁸, K. Velmourougane ⁸, A. Srivastava ⁹, Mausumi Raychaudhuri ¹⁰, D. K. Kundu ¹⁰, K. G. Mandal ¹⁰, G. Kar ¹⁰, S. L. Durge ¹, G. K. Kamble ¹, M. S. Gaikwad ¹, A. M. Nimkar ¹, S. V. Bobade ¹, S. G. Anantwar ¹, S. Patil ¹, V. T. Sahu ¹, K. M. Gaikwad ¹, H. Bhondwe ¹, S. S. Dohtre ¹, S. Gharami ¹, S. G. Khapekar ¹, A. Koyal ³, Sujatha ³, B. M. N. Reddy ³, P. Sreekumar ³, D. P. Dutta ⁶, L. Gogoi ⁶, V. N. Parhad ¹, A. S. Halder ⁴, R. Basu ⁴, R. Singh ⁵, B. L. Jat ⁵, D. L. Oad ⁵, N. R. Ola ⁵, K. Wadhai ¹, M. Lokhande ¹, V. T. Dongare ¹, A. Hukare ¹, N. Bansod ¹, A. Kolhe ¹, J. Khuspure ¹, H. Kuchankar ¹, D. Balbuddhe ¹, S. Sheikh ¹, B. P. Sunitha ³, B. Mohanty ², D. Hazarika ⁶, S. Majumdar ⁴, R. S. Garhwal ⁵, A. Sahu ⁸, S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, A. Kumar ⁹, N. Gautam ¹, B. A. Telpande ¹, A. M. Nimje ¹, C. Likhar ¹, S. Thakre ¹

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
2 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
3 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091
8 Central Institute for Cotton Research, Nagpur 440 010, IN
9 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
10 Directorate of Water Management, Bhubaneswar 751 023, IN

Abstract

The articles presented in this special section emanated from the researches of consortium members of the National Agricultural Innovative Project (NAIP, Component 4) of the Indian Council of Agricultural Research (ICAR), New Delhi. These researches have helped develop a soil information system (SIS). In view of the changing scenario all over the world, the need of the hour is to get assistance from a host of researchers specialized in soils, crops, geology, geography and information technology to make proper use of the datasets. Equipped with the essential knowledge of data storage and retrieval for management recommendations, these experts should be able to address the issues of land degradation, biodiversity, food security, climate change and ultimately arrive at an appropriate agricultural land-use planning. Moreover, as the natural resource information is an essential prerequisite for monitoring and predicting global environmental change with special reference to climate and land use options, the SIS needs to be a dynamic exercise to accommodate temporal datasets, so that subsequently it should result in the evolution of the soil information technology. The database developed through this NAIP would serve as an example of the usefulness of the Consortium and the research initiative of ICAR involving experts from different fields to find out the potentials of the soils of humid and semi-arid bioclimatic systems of the country.

Keywords

Agricultural Land-Use Planning, Humid and Semi-Arid Tropics, Soil Information System, Soil Information Technology, Temporal Datasets.

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P. Tiwary ¹, N. G. Patil ¹, T. Bhattacharyya ¹, P. Chandran ¹, S. K. Ray ¹, K. Karthikeyan ¹, D. Sarkar ¹, D. K. Pal ², J. Prasad ¹, C. Mandal ¹, D. K. Mandal ¹, G. S. Sidhu ³, K. M. Nair ⁴, A. K. Sahoo ⁵, T. H. Das ⁵, R. S. Singh ⁶, R. Srivastava ¹, T. K. Sen ¹, S. Chatterji ¹, G. P. Obireddy ¹, S. K. Mahapatra ³, K. S. Anil Kumar ⁴, K. Das ⁵, A. K. Singh ⁶, S. K. Reza ⁷, D. Dutta ⁵, S. Srinivas ⁴, M. V. Venugopalan ⁸, K. Velmourougane ⁸, A. Srivastava ⁹, M. Raychaudhuri ¹⁰, D. K. Kundu ¹⁰, K. G. Mandal ¹⁰, G. Kar ¹⁰, S. L. Durge ¹, G. K. Kamble ¹, M. S. Gaikwad ¹, A. M. Nimkar ¹, S. V. Bobade ¹, S. G. Anantwar ¹, S. Patil ¹, K. M. Gaikwad ¹, V. T. Sahu ¹, H. Bhondwe ¹, S. S. Dohtre ¹, S. Gharami ¹, S. G. Khapekar ¹, A. Koyal ⁴, K. Sujatha ⁴, B. M. N. Reddy ⁴, P. Sreekumar ⁴, D. P. Dutta ⁷, L. Gogoi ⁷, V. N. Parhad ¹, A. S. Halder ⁵, R. Basu ⁵, R. Singh ⁶, B. L. Jat ⁶, D. L. Oad ⁶, N. R. Ola ⁶, K. Wadhai ¹, M. Lokhande ¹, V. T. Dongare ¹, A. Hukare ¹, N. Bansod ¹, A. H. Kolhe ¹, J. Khuspure ¹, H. Kuchankar ¹, D. Balbuddhe ¹, S. Sheikh ¹, B. P. Sunitha ⁴, B. Mohanty ³, D. Hazarika ⁷, S. Majumdar ⁵, R. S. Garhwal ⁶, A. Sahu ⁸, S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, A. Kumar ⁹, N. Gautam ¹, B. A. Telpande ¹, A. M. Nimje ¹, C. Likhar ¹, S. Thakre ¹

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
3 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
8 Central Institute for Cotton Research, Nagpur 440 010, IN
9 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
10 Directorate of Water Management, Bhubaneswar 751 023, IN

Abstract

In recent years, georeferenced soil information system has gained significance in agricultural land-use planning and monitoring the changes in soil properties/ soil quality induced by land-use changes. The spatiotemporal information on saturated hydraulic conductivity (sHC) and soil water retention-release behaviour is essential for proper crop and land-use planning. The sHC greatly influences the drainage process and soil water retention-release behaviour, ultimately affecting the crop growth and yield. However, sHC and water retention are not measured in a routine soil survey and are generally estimated from easily measurable soil parameters through pedotransfer functions (PTFs). In the present study, PTFs for sHC and water retention were developed separately for the soils of two food-growing zones of India (the Indo-Gangetic Plains (IGP) and the black soil region (BSR)). For the IGP soils, sHC is affected by the increased subsoil bulk density due to intensive cultivation. In BSR, presence of Na⁺ and Mg⁺⁺ ions affects the drainage and water retention of the soils. Therefore, these soil parameters were considered while developing the PTFs using stepwise regression technique in SPSS. The validation of PTFs was found to be satisfactory with low RMSE values and high model efficiency.

Keywords

Model Efficiency, Pedotransfer Functions, Regression Analysis, Saturated Hydraulic Conductivity, Water Retention.

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N. G. Patil ¹, P. Tiwary ¹, T. Bhattacharyya ¹, P. Chandran ¹, D. Sarkar ¹, D. K. Pal ², D. K. Mandal ¹, J. Prasad ¹, G. S. Sidhu ³, K. M. Nair ⁴, A. K. Sahoo ⁵, T. H. Das ⁵, R. S. Singh ⁶, C. Mandal ¹, R. Srivastava ¹, T. K. Sen ¹, S. Chatterji ¹, S. K. Ray ¹, G. P. Obireddy ¹, S. K. Mahapatra ³, K. S. Anil Kumar ⁴, K. Das ⁵, A. K. Singh ⁶, S. K. Reza ⁷, D. Dutta ⁵, S. Srinivas ⁴, K. Karthikeyan ⁴, M. V. Venugopalan ⁸, K. Velmourougane ⁸, A. Srivastava ⁹, M. Raychaudhuri ¹⁰, D. K. Kundu ¹¹, K. G. Mandal ¹⁰, G. Kar ¹⁰, S. L. Durge ¹, G. K. Kamble ¹, M. S. Gaikwad ¹, A. M. Nimkar ¹, S. V. Bobade ¹, S. G. Anantwar ¹, S. Patil ¹, K. M. Gaikwad ¹, V. T. Sahu ¹, H. Bhondwe ¹, S. S. Dohtre ¹, S. Gharami ¹, S. G. Khapekar ¹, A. Koyal ⁴, K. Sujatha ⁴, B. M. N. Reddy ⁴, P. Sreekumar ⁴, D. P. Dutta ⁷, L. Gogoi ⁷, V. N. Parhad ¹, A. S. Halder ⁵, R. Basu ⁵, R. Singh ⁶, B. L. Jat ⁶, D. L. Oad ⁶, N. R. Ola ⁶, K. Wadhai ¹, M. Lokhande ¹, V. T. Dongare ¹, A. Hukare ¹, N. Bansod ¹, A. H. Kolhe ¹, J. Khuspure ¹, H. Kuchankar ¹, D. Balbuddhe ¹, S. Sheikh ¹, B. P. Sunitha ⁴, B. Mohanty ³, D. Hazarika ⁷, S. Majumdar ⁵, R. S. Garhwal ⁶, A. Sahu ⁸, S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, A. Kumar ⁹, N. Gautam ¹, B. A. Telpande ¹, A. M. Nimje ¹, C. Likhar ¹, S. Thakre ¹

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
3 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 440 010, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
8 Central Institute for Cotton Research, Nagpur 440 010, IN
9 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
10 Directorate of Water Management, Bhubaneswar 751 023, IN
11 Directorate of Water Management, Bhubaneswar 751 023

Abstract

Current status of land/soil resources of the Indo- Gangetic Plains (IGP) is analysed to highlight the issues that need to be tackled in near future for sustained agricultural productivity. There are intraregional variations in soil properties, cropping systems; status of land usage, groundwater utilization and irrigation development which vary across the subregions besides demographies. Framework for land use policy is suggested that includes acquisition of farm-level data, detailing capability of each unit to support a chosen land use, assess infrastructural support required to meet the projected challenges and finally develop skilled manpower to effectively monitor the dynamics of land use changes.

Keywords

Agricultural Productivity, Land Use Planning, Natural Resources, Soil Properties and Soil Management.

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Mausumi Raychaudhuri ¹, D. K. Kundu ¹, Ashwani Kumar ¹, K. G. Mandal ¹, S. Raychaudhuri ¹, G. Kar ¹, T. Bhattacharyya ², D. Sarkar ³, D. K. Pal ⁴, D. K. Mandal ³, J. Prasad ³, G. S. Sidhu ⁵, K. M. Nair ⁶, A. K. Sahoo ⁷, T. H. Das ⁷, R. S. Singh ⁸, C. Mandal ³, R. Srivastava ³, T. K. Sen ³, S. Chatterji ³, P. Chandran ³, S. K. Ray ³, N. G. Patil ³, G. P. Obireddy ³, S. K. Mahapatra ⁵, K. S. Anil Kumar ⁶, K. Das ⁵, A. K. Singh ⁸, S. K. Reza ⁹, D. Dutta ⁷, S. Srinivas ⁶, P. Tiwary ³, K. Karthikeyan ³, M. V. Venugopalan ⁹, K. Velmourougane ⁹, A. Srivastava ¹⁰, S. L. Durge ³, S. Puspamitra ¹, S. Mahapatra ¹, G. K. Kamble ³, M. S. Gaikwad ³, A. M. Nimkar ³, S. V. Bobade ³, S. G. Anantwar ³, S. Patil ³, K. M. Gaikwad ³, V. T. Sahu ³, H. Bhondwe ³, S. S. Dohtre ³, S. Gharami ³, S. G. Khapekar ³, A. Koyal ⁵, Sujatha ⁵, B. M. N. Reddy ⁵, P. Sreekumar ⁵, D. P. Dutta ⁹, L. Gogoi ⁹, V. N. Parhad ¹, A. S. Halder ⁷, R. Basu ⁷, R. Singh ⁷, B. L. Jat ⁷, D. L. Oad ⁷, N. R. Ola ⁷, K. Wadhai ³, M. Lokhande ³, V. T. Dongare ³, A. Hukare ³, N. Bansod ³, A. Kolhe ³, J. Khuspure ³, H. Kuchankar ³, D. Balbuddhe ³, S. Sheikh ³, B. P. Sunitha ⁶, B. Mohanty ⁵, D. Hazarika ⁹, S. Majumdar ⁷, R. S. Garhwal ⁸, A. Sahu ¹¹, A. Kumar ¹⁰, N. Gautam ³, B. A. Telpande ³, A. M. Nimje ³, C. Likhar ³, S. Thakre ³

Affiliations
1 Directorate of Water Management, Bhubaneswar, Odisha 751 023, IN
2 2Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
3 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
4 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
8 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
9 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
10 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
11 Central Institute for Cotton Research, Nagpur 440 010, IN

Abstract

Understanding the physical quality of soil that influences its hydraulic behaviour helps in formulating appropriate water management strategies for sustainable crop production. Saturated hydraulic conductivity (K_s) is a key factor governing the hydraulic properties of soils. K_s can be estimated through various techniques. In the present article we have developed and validated the regression models to predict K_s of the soils of the Indo- Gangetic Plains (IGP) and the black soil regions (BSR) under different bioclimatic systems. While particle size distribution was found to be a key factor to predict K_s of the BSR soils, organic carbon was found useful for the IGP soils. Moreover, the models for K_s of both soils were strengthened by putting in CaCO₃ and exchangeable sodium percentage content. It seems there is ample scope to study the interaction process for revising K_s to desired levels through management practices in these two important food-growing zones. An index of soil physical quality, derived from the inflection points of the soil moisture characteristic curves could well explain the impact of management practices on soil physical quality.

Keywords

Index, Management, Saturated Hydraulic Conductivity, Soil Physical Quality.

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K. Velmourougane ¹, M. V. Venugopalan ¹, T. Bhattacharyya ², D. Sarkar ², S. K. Ray ², P. Chandran ², D. K. Pal ³, D. K. Mandal ², J. Prasad ², G. S. Sidhu ⁴, K. M. Nair ⁵, A. K. Sahoo ⁶, K. S. Anil Kumar ⁵, A. Srivastava ⁷, T. H. Das ⁶, R. S. Singh ⁸, C. Mandal ², R. Srivastava ², T. K. Sen ², S. Chatterji ², N. G. Patil ², G. P. Obireddy ², S. K. Mahapatra ⁴, K. Das ⁶, S. K. Singh ⁶, S. K. Reza ⁹, D. Dutta ⁶, S. Srinivas ⁵, P. Tiwary ², K. Karthikeyan ², Mausumi Raychaudhuri ¹⁰, D. K. Kundu ¹⁰, K. G. Mandal ¹⁰, G. Kar ¹⁰, S. L. Durge ², G. K. Kamble ², M. S. Gaikwad ², A. M. Nimkar ², S. V. Bobade ², S. G. Anantwar ², S. Patil ², M. S. Gaikwad ², V. T. Sahu ², H. Bhondwe ², S. S. Dohtre ², S. Gharami ², S. G. Khapekar ², A. Koyal ⁵, Sujatha ⁵, B. M. N. Reddy ⁵, P. Sreekumar ⁵, D. P. Dutta ⁹, L. Gogoi ⁹, V. N. Parhad ², A. S. Halder ⁶, R. Basu ⁶, R. Singh ⁸, B. L. Jat ⁸, D. L. Oad ⁸, N. R. Ola ⁸, A. Sahu ², K. Wadhai ², M. Lokhande ², V. T. Dongare ², A. Hukare ², N. Bansod ², A. Kolhe ², J. Khuspure ², H. Kuchankar ², D. Balbuddhe ², S. Sheikh ², B. P. Sunitha ⁵, B. Mohanty ⁴, D. Hazarika ⁹, S. Majumdar ⁶, R. S. Garhwal ⁸, S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, A. Kumar ⁷, N. Gautam ², B. A. Telpande ², A. M. Nimje ², C. Likhar ², S. Thakre ²

Affiliations
1 Central Institute for Cotton Research, Nagpur 440 010, IN
2 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
3 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
7 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
8 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
9 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
10 Directorate of Water Management, Bhubaneswar 751 023, IN

Abstract

The present study documents the biological properties of the black soil region (BSR) of India in terms of culturable microbial population. Besides surface microbial population, subsurface population of individual soil horizons is described to improve the soil information system. An effort has been made to study the depth-wise distribution and factors (bioclimates, cropping systems, land use, management practices and soil properties) influencing the microbial population in the soils of the selected benchmark spots representing different agro-ecological sub-regions of BSR. The microbial population declined with depth and maximum activity was recorded within 0-30 cm soil depth. The average microbial population (log₁₀ cfu g^-1) in different bioclimates is in decreasing order of SHm > SHd > Sad > arid. Within cropping systems, legumebased system recorded higher microbial population (6.12 log₁₀ cfu g^-1) followed by cereal-based system (6.09 log₁₀ cfu g^-1). The mean microbial population in different cropping systems in decreasing order is legume > cereal > sugarcane > cotton. Significantly higher (P < 0.05) microbial population has been recorded in high management (6.20 log₁₀ cfu g^-1) and irrigated agrosystems (6.33 log₁₀ cfu g^-1) compared to low management (6.12 log₁₀ cfu g^-1) and rainfed agrosystems (6.17 log₁₀ cfu g^-1). The pooled analysis of data inclusive of bioclimates, cropping systems, land use, management practices, and edaphic factors indicates that microbial population is positively influenced by clay, fine clay, water content, electrical conductivity, organic carbon, cation exchange capacity and base saturation, whereas bulk density, pH, calcium carbonate and exchangeable magnesium percentage have a negative effect on the microbial population.

Keywords

Agro-Ecological Sub-Regions, Benchmark Spots, Black Soil Regions, Principal Component Analysis, Soil Microbial Population.

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C. Mandal ¹, D. K. Mandal ¹, T. Bhattacharyya ², D. Sarkar ², D. K. Pal ², Jagdish Prasad ², G. S. Sidhu ³, K. M. Nair ⁴, A. K. Sahoo ⁵, T. H. Das ⁶, R. S. Singh ⁷, R. Srivastava ², T. K. Sen ², S. Chatterji ², P. Chandran ², S. K. Ray ², N. G. Patil ², G. P. Obireddy ², S. K. Mahapatra ⁶, K. S. Anil Kumar ⁴, K. Das ⁶, A. K. Singh ⁷, S. K. Reza ⁸, D. Dutta ⁶, S. Srinivas ⁴, P. Tiwary ², K. Karthikeyan ², M. V. Venugopalan ⁹, A. Srivastava ¹⁰, Mausumi Raychaudhuri ¹¹, D. K. Kundu ¹¹, K. G. Mandal ¹¹, G. Kar ¹¹, S. L. Durge ², G. K. Kamble ², M. S. Gaikwad ², A. M. Nimkar ², S. V. Bobade ², S. G. Anantwar ², S. Patil ², K. M. Gaikwad ², A. M. Nimkar ², S. V. Bobade ², S. G. Anantwar ², S. Patil ², K. M. Gaikwad ², V. T. Sahu ², H. Bhondwe ², S. S. Dohtre ², S. Gharami ², S. G. Khapekar ², A. Koyal ⁴, Sujatha ⁴, B. M. N. Reddy ⁴, P. Sreekumar ⁴, D. P. Dutta ⁸, L. Gogoi ¹², V. N. Parhad ², A. S. Halder ⁶, R. Basu ⁶, R. Singh ⁷, B. L. Jat ¹³, D. L. Oad ⁷, N. R. Ola ⁷, K. Wadhai ², M. Lokhande ², V. T. Dongare ², A. Hukare ², N. Bansod ², A. Kolhe ², J. Khuspure ², H. Kuchankar ², D. Balbuddhe ², S. Sheikh ², B. P. Sunitha ⁴, B. Mohanty ³, D. Hazarika ⁸, S. Majumdar ⁶, R. S. Garhwal ⁷, A. Sahu ⁹, S. Mahapatra ¹¹, S. Puspamitra ¹¹, A. Kumar ¹⁰, N. Gautam ², B. A. Telpande ², A. M. Nimje ², C. Likhar ², S. Thakre ²

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 03, IN
2 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
3 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
8 National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, IN
9 Central Institute for Cotton Research, Nagpur 440 010, IN
10 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
11 Directorate of Water Management, Bhubaneswar 751 023, IN
12 National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004
13 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001

Abstract

The sustenance of food and nutritional security are the major challenges of the 21st century. The domestic food production needs to increase per annum at the rate of 2% for cereals and 0.6% for oilseeds and pulses to meet the demand by 2030. The Indo-Gangetic Plains (IGP) and the black soil regions (BSR) are the two major food production zones of the country. Since irrigation potential is limited and expansion of irrigated area is tardy, rainfed agriculture holds promise to satisfy future food needs. Frontline demonstrations of these two regions have shown that there is a large gap at the farmers' and achievable levels of yields. This gap can be filled by adopting scientific approach of managing the natural resources. There is tremendous pressure of biotic and abiotic stresses hindering the crop production and that warrants for a systematic appraisal of natural resources. The National Bureau of Soil Survey and Land Use Planning (NBSS&LUP) under the Indian Council of Agricultural Research (ICAR) divided the country into 60 agro-ecological sub-regions (AESRs) in 1994 by superimposing maps on natural resources like soils, climate and length of growing period (LGP) for crops and other associated parameters. With the passage of nearly two decades and the advent of modern facilities of database management and improved knowledge base on natural resources, a need was felt to revise the existing AESR map to reach near the ground reality of crop performance. The new database stored in soil and terrain digital database (SOTER) has helped in modifying the AESR delineations of the BSR (76.4 m ha) and the IGP (52.01 m ha). The estimated available water content, saturated hydraulic conductivity and use of pedo-transfer functions in assessing the drainage conditions and soil quality have helped in computing with improved precision the LGP, and revise the earlier AESRs in BSR and IGP areas. This innovative exercise will be useful for the future AESR-based agricultural land use planning.

Keywords

Agro-Ecological Sub-Regions, Food Production Zones, Land-Use Planning, Length of Growing Period.

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M. V. Venugopalan ¹, P. Tiwary ², S. K. Ray ², S. Chatterji ², K. Velmourougane ¹, T. Bhattacharyya ², K. K. Bandhopadhyay ³, D. Sarkar ², P. Chandran ², D. K. Pal ⁴, D. K. Mandal ², J. Prasad ², G. S. Sidhu ⁵, K. M. Nair ⁶, A. K. Sahoo ⁷, K. S. Anil Kumar ⁶, A. Srivastava ⁸, T. H. Das ⁷, R. S. Singh ⁹, C. Mandal ², R. Srivastava ², T. K. Sen ², N. G. Patil ², G. P. Obireddy ², S. K. Mahapatra ⁵, K. Das ⁷, S. K. Singh ⁷, S. K. Reza ¹⁰, D. Dutta ⁷, S. Srinivas ⁶, K. Karthikeyan ², Mausumi Raychaudhuri ¹¹, D. K. Kundu ¹¹, K. K. Mandal ¹¹, G. Kar ¹¹, S. L. Durge ², G. K. Kamble ², M. S. Gaikwad ², A. M. Nimkar ², S. V. Bobade ², S. G. Anantwar ², S. Patil ¹², M. S. Gaikwad ², V. T. Sahu ², H. Bhondwe ², S. S. Dohtre ², S. Gharami ², S. G. Khapekar ², A. Koyal ⁶, Sujatha ⁶, B. M. N. Reddy ⁶, P. Sreekumar ⁶, D. P. Dutta ¹⁰, L. Gogoi ¹⁰, V. N. Parhad ², A. S. Halder ¹³, R. Basu ⁷, R. Singh ⁹, B. L. Jat ⁹, D. L. Oad ⁹, N. R. Ola ⁹, A. Sahu ¹, K. Wadhai ², M. Lokhande ², V. T. Dongare ², A. Hukare ², N. Bansod ², A. Kolhe ², J. Khuspure ², H. Kuchankar ², D. Balbuddhe ², S. Sheikh ², B. P. Sunitha ⁶, B. Mohanty ⁵, D. Hazarika ¹⁰, S. Majumdar ⁷, R. S. Garhwal ⁹, S. Mahapatra ¹¹, S. Puspamitra ¹¹, A. Kumar ⁸, N. Gautam ², B. A. Telpande ², A. M. Nimje ², C. Likhar ², S. Thakre ²

Affiliations
1 Central Institute for Cotton Research, Nagpur 440 010, IN
2 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
3 Indian Agricultural Research Institute, New Delhi 110 012, IN
4 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
8 National Bureau of Agriculturally Important Microorganisms, Mau 275 103, IN
9 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
10 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
11 Directorate of Water Management, Bhubaneswar 751 023, IN
12 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033
13 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091

Abstract

Crop simulation models have emerged as powerful tools for estimating yield gaps, forecasting production of agricultural crops and analysing the impact of climate change. In this study, the genetic coefficients for Bt hybrids established from field experiments were used in the InfoCrop-cotton model, which was calibrated and validated earlier to simulate the cotton production under different agro-climatic conditions. The model simulated results for Bt hybrids were satisfactory with an R² value of 0.55 (n = 22), d value of 0.85 and a ischolar_main mean square error of 277 kg ha^-1, which was 11.2% of the mean observed. Relative yield index (RYI) defined as the ratio between simulated rainfed (water-limited) yield to potential yield, was identified as a robust land quality index for rainfed cotton. RYI was derived for 16 representative benchmark (BM) locations of the black soil region from long-term simulation results of InfoCrop-cotton model (based on 11-40 years of weather data). The model could satisfactorily capture subtle differences in soil variables and weather patterns prevalent in the BM locations spread over 16 agro-ecological sub-regions (AESRs) resulting in a wide range of mean simulated rainfed cotton yields (482-4393 kg ha^-1). The BM soils were ranked for their suitability for cotton cultivation based on RYI. The RYI of black soils (vertisols) ranged from 0.07 in Nimone to 0.80 in Panjari representing AESR (6.1) and AESR (10.2) respectively, suggesting that Panjri soils are better suited for rainfed cotton.

Keywords

Bt Cotton, Land Quality, Relative Yield Index, Simulation Model.

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S. K. Ray ¹, T. Bhattacharyya ¹, K. R. Reddy ², D. K. Pal ³, P. Chandran ¹, P. Tiwary ¹, D. K. Mandal ¹, C. Mandal ¹, J. Prasad ¹, D. Sarkar ¹, M. V. Venugopalan ⁴, K. Velmourougane ⁴, G. S. Sidhu ⁵, K. M. Nair ⁶, A. K. Sahoo ⁷, T. H. Das ⁷, R. S. Singh ⁸, R. Srivastava ¹, T. K. Sen ¹, S. Chatterji ¹, N. G. Patil ¹, G. P. Obireddy ¹, S. K. Mahapatra ⁵, K. S. Anil Kumar ⁶, K. Das ⁷, S. K. Reza ⁹, D. Dutta ⁹, S. Srinivas ⁶, K. Karthikeyan ¹, A. Srivastava ¹⁰, M. Raychaudhuri ¹¹, D. K. Kundu ¹¹, V. T. Dongare ¹, D. Balbuddhe ¹, N. G. Bansod ¹, K. Wadhai ¹, M. Lokhande ¹, A. Kolhe ¹, H. Kuchankar ¹, S. L. Durge ¹, G. K. Kamble ¹, M. S. Gaikwad ¹, A. M. Nimkar ¹, S. V. Bobade ¹, S. G. Anantwar ¹, S. Patil ¹, V. T. Sahu ¹, S. Sheikh ¹, D. Dasgupta ¹, B. A. Telpande ¹, A. M. Nimje ¹, C. Likhar ¹, S. Thakre ¹, K. G. Mandal ¹⁰, G. Kar ¹⁰, K. M. Gaikwad ¹, H. Bhondwe ¹, S. S. Dohtre ¹, S. Gharami ¹, S. G. Khapekar ¹, A. Koyal ⁴, Sujatha ⁴, B. M. N. Reddy ⁴, P. Sreekumar ⁴, D. P. Dutta ⁷, L. Gogoi ⁷, V. N. Parhad ¹, A. S. Halder ⁵, R. Basu ⁵, R. Singh ⁶, B. L. Jat ⁶, D. L. Oad ⁶, N. R. Ola ⁶, A. Hukare ¹, J. Khuspure ¹, B. P. Sunitha ⁴, B. Mohanty ³, D. Hazarika ⁷, S. Majumdar ⁵, R. S. Garhwal ⁶, A. Sahu ⁸, S. Mahapatra ¹¹, S. Puspamitra ¹¹, A. Kumar ⁹, N. Gautam ¹

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
2 Institute of Food and Agricultural Sciences, Soil and Water Science Department, University of Florida, Gainesville, Florida, US
3 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
4 Central Institute for Cotton Research, Nagpur 440 010, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
8 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
9 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
10 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
11 Directorate of Water Management, Bhubaneswar 751 023, IN

Abstract

Sustaining soil and land quality under intensive land use and fast economic development is a major challenge for improving crop productivity in the developing world. Assessment of soil and land quality indicators is necessary to evaluate the degradation status and changing trends of different land use and management interventions. During the last four decades, the Indo-Gangetic Plains (IGP) which covers an area of about 52.01 m ha has been the major food producing region of the country. However at present, the yield of crops in IGP has stagnated; one of the major reasons being deterioration of soil and land quality. The present article deals with the estimation of soil and land quality indicators of IGP, so that, proper soil and land management measures can be taken up to restore and improve the soil health. Use of principal component analysis is detailed to derive the minimum dataset or indicators for soil quality. The article also describes spatial distribution of soil and land quality with respect to major crops of IGP.

Keywords

Land Quality Index, Principal Component Analysis, Soil Quality and Health.

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S. Chatterji ¹, P. Tiwary ¹, T. K. Sen ¹, J. Prasad ¹, T. Bhattacharyya ¹, D. Sarkar ¹, D. K. Pal ², D. K. Mandal ¹, G. S. Sidhu ³, K. M. Nair ⁴, A. K. Sahoo ⁵, T. H. Das ⁵, R. S. Singh ⁶, C. Mandal ¹, R. Srivastava ¹, P. Chandran ¹, S. K. Ray ¹, N. G. Patil ¹, G. P. Obireddy ¹, S. K. Mahapatra ³, S. Srinivas ⁴, K. Das ⁵, A. K. Singh ⁶, S. K. Reza ⁷, D. Dutta ⁵, K. S. Anil Kumar ⁴, K. Karthikeyan ¹, M. V. Venugopalan ⁸, K. Velmourougane ⁸, A. Srivastava ⁹, Mausumi Raychaudhuri ¹⁰, D. K. Kundu ¹⁰, K. G. Mandal ¹⁰, G. Kar ¹⁰, S. L. Durge ¹, G. K. Kamble ¹, M. S. Gaikwad ¹, A. M. Nimkar ¹, S. V. Bobade ¹, S. G. Anantwar ¹, S. Patil ¹, K. M. Gaikwad ¹, V. T. Sahu ¹, H. Bhondwe ¹, S. S. Dohtre ¹, S. Gharami ¹, S. G. Khapekar ¹, A. Koyal ⁴, Sujatha ⁴, B. M. N. Reddy ⁴, P. Sreekumar ⁴, D. P. Dutta ⁴, L. Gogoi ⁷, V. N. Parhad ¹, A. S. Halder ⁵, R. Basu ⁵, R. Singh ⁶, B. L. Jat ⁶, D. L. Oad ⁶, N. R. Ola ⁶, K. Wadhai ¹, M. Lokhande ¹, V. T. Dongare ¹, A. Hukare ¹, N. Bansod ¹, A. Kolhe ¹, J. Khuspure ¹, H. Kuchankar ¹, D. Balbuddhe ¹, S. Sheikh ¹, B. P. Sunitha ⁴, B. Mohanty ³, D. Hazarika ⁷, S. Majumdar ⁵, R. S. Garhwal ⁶, A. Sahu ⁸, S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, A. Kumar ⁹, N. Gautam ¹, B. A. Telpande ¹, A. M. Nimje ¹, C. Likhar ¹, S. Thakre ¹

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
3 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
8 Central Institute for Cotton Research, Nagpur 440 010, IN
9 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
10 Directorate of Water Management, Bhubaneswar 751 023, IN

Abstract

Land evaluation is carried out to assess the suitability of land for a specific use. Land evaluation procedures focus increasingly on the use of quantitative procedures to enhance the qualitative interpretation of land resource surveys. Conventional Boolean retrieval of soil survey data and logical models for assessing land suitability, treat both spatial units and attribute value ranges as exactly specifiable quantities. They ignore the continuous nature of soil and landscape variation and uncertainties in measurement, which may result in the failure to correctly classify sites that just fail to match strictly defined requirements. The objective of this article is to apply fuzzy model to land suitability evaluation for major crops in the 15 benchmark sites of the Indo- Gangetic Plains (IGP) and 17 benchmark sites of the black soil regions (BSR). Minimum datasets of land characteristics considered relevant to rice and wheat in the IGP and cotton and soybean in the BSR were identified to enhance pragmatic value of land evaluation. The use of fuzzy model is intuitive, robust and helpful for land suitability evaluation and classification, especially in applications in which subtle differences in land characteristics are of a major interest, such as development of threshold values of land characteristics.

Keywords

Benchmark Sites, Fuzzy Model, Land Evaluation, Minimum Datasets.

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G. S. Sidhu ¹, T. Bhattacharyya ², D. Sarkar ², S. K. Ray ², P. Chandran ², D. K. Pal ³, D. K. Mandal ², J. Prasad ², K. M. Nair ⁴, A. K. Sahoo ⁵, T. H. Das ⁵, R. S. Singh ⁶, C. Mandal ², R. Srivastava ², T. K. Sen ², S. Chatterji ², N. G. Patil ², G. P. Obireddy ², S. K. Mahapatra ³, K. S. Anil Kumar ⁴, K. Das ⁵, A. K. Singh ⁶, S. K. Reza ⁷, D. Dutta ⁵, S. Srinivas ⁴, P. Tiwary ², K. Karthikeyan ², M. V. Venugopalan ⁸, K. Velmourougane ⁸, A. Srivastava ⁹, Mausumi Raychaudhuri ¹⁰, D. K. Kundu ¹⁰, K. G. Mandal ¹⁰, G. Kar ¹⁰, S. L. Durge ², G. K. Kamble ², M. S. Gaikwad ², A. M. Nimkar ², S. V. Bobade ², S. G. Anantwar ², S. Patil ², V. T. Sahu ², K. M. Gaikwad ², H. Bhondwe ², S. S. Dohtre ², S. Gharami ², S. G. Khapekar ², A. Koyal ⁴, Sujatha ⁴, B. M. N. Reddy ⁴, P. Sreekumar ⁴, D. P. Dutta ⁷, L. Gogoi ⁷, V. N. Parhad ², A. S. Halder ⁵, R. Basu ⁵, R. Singh ⁶, B. L. Jat ⁶, D. L. Oad ⁶, N. R. Ola ⁶, K. Wadhai ², M. Lokhande ², V. T. Dongare ², A. Hukare ², N. Bansod ², A. Kolhe ², J. Khuspure ², H. Kuchankar ², D. Balbuddhe ², S. Sheikh ², B. P. Sunitha ⁴, B. Mohanty ³, D. Hazarika ⁷, S. Majumdar ⁵, R. S. Garhwal ⁶, A. Sahu ⁸, S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, A. Kumar ⁹, N. Gautam ², B. A. Telpande ², A. M. Nimje ², C. Likhar ², S. Thakre ²

Affiliations
1 Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi 110 012, IN
2 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
3 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
4 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Bangalore 560 024, IN
5 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Kolkata 700 091, IN
6 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Udaipur 313 001, IN
7 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN
8 Central Institute for Cotton Research, Nagpur 440 010, IN
9 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
10 Directorate of Water Management, Bhubaneswar 751 023, IN

Abstract

Five benchmark soils, namely Fatehpur (Punjab) and Haldi (Uttarakhand) non-sodic soils, Zarifa Viran (Haryana), Sakit and Itwa sodic soils (Uttar Pradesh) representing Trans, Upper, Middle and Central Indo- Gangetic Plains (IGP) were revisited for studying the morphological, physical and chemical properties of soils at low and high management levels to monitor changes in soil properties due to the impact of landuse as well as management levels. The results indicate an increase in bulk density (BD) below the plough layer, and build up of organic carbon (OC) and decline in pH in surface layers of Zarifa Viran, Sakit and Itwa sodic soils under high management. The concentration of carbonates and bicarbonates in sodic soils decreased due to adaptation of rice-wheat system. The build-up of OC and decrease of pH in surface soils under rice- wheat system enhanced the soil health. Increase in BD in subsurface soils, however, is a cause of concern for sustaining rice-wheat cropping system. Soil management interventions such as tillage, conservation agriculture and alternate cropping system have been suggested for improved soil health and productivity.

Keywords

Benchmark Soil, Bulk Density, Land-Use Changes, Rice–Wheat System, Soil Properties.

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Sachin D. Ghude ¹, G. S. Bhat ², Thara Prabhakaran ¹, R. K. Jenamani ³, D. M. Chate ¹, P. D. Safai ¹, A. K. Karipot ⁴, M. Konwar ¹, Prakash Pithani ¹, V. Sinha ⁵, P. S. P. Rao ¹, S. A. Dixit ¹, S. Tiwari ¹, K. Todekar ¹, S. Varpe ¹, A. K. Srivastava ¹, D. S. Bisht ¹, P. Murugavel ¹, Kaushar Ali ¹, Usha Mina ⁶, M. Dharua ¹, J. Rao ¹, B. Padmakumari ¹, A. Hazra ¹, N. Nigam ³, U. Shende ³, D. M. Lal ¹, B. P. Chandra ⁵, A. K. Mishra ⁵, A. Kumar ⁵, H. Hakkim ⁵, H. Pawar ⁵, P. Acharja ¹, Rachana Kulkarni ¹, C. Subharthi ¹, B. Balaji ¹, M. Varghese ¹, S. Bera ¹, M. Rajeevan ⁷

Affiliations
1 Indian Institute of Tropical Meteorology, Pashan, Pune 411 008, IN
2 Indian Institute of Science, Bengaluru 560 012, IN
3 India Meteorological Department, New Delhi 110 003, IN
4 Savitribai Phule Pune University, Pune 411 007, IN
5 Indian Institute of Science Education and Research Mohali, Mohali 140 306, IN
6 Indian Agricultural Research Institute, Pusa, New Delhi 110 012, IN
7 Ministry of Earth Sciences, Government of India, New Delhi 110 003, IN

Abstract

The objectives of the Winter Fog Experiment (WIFEX) over the Indo-Gangetic Plains of India are to develop better now-casting and forecasting of winter fog on various time- and spatial scales. Maximum fog occurrence over northwest India is about 48 days (visibility <1000 m) per year, and it occurs mostly during the December-February time-period. The physical and chemical characteristics of fog, meteorological factors responsible for its genesis, sustenance, intensity and dissipation are poorly understood. Improved understanding on the above aspects is required to develop reliable forecasting models and observational techniques for accurate prediction of the fog events. Extensive sets of comprehensive ground-based instrumentation were deployed at the Indira Gandhi International Airport, New Delhi. Major in situ sensors were deployed to measure surface micro-meteorological conditions, radiation balance, turbulence, thermodynamical structure of the surface layer, fog droplet and aerosol microphysics, aerosol optical properties, and aerosol and fog water chemistry to describe the complete environmental conditions under which fog develops. In addition, Weather Forecasting Model coupled with chemistry is planned for fog prediction at a spatial resolution of 2 km. The present study provides an introductory overview of the winter fog field campaign with its unique instrumentation.

Keywords

Aerosols, Atmospheric Profiles, Forecasting, Winter Fog.

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K. K. Singh ¹, H. S. Negi ¹, A. Kumar ², A. V. Kulkarni ³, S. K. Dewali ¹, P. Datt ¹, A. Ganju ¹, S. Kumar ¹

Affiliations
1 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
2 National Institute of Technology, Kurukshetra 136 119, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN

Abstract

In this study an airborne ground penetrating radar (GPR) is used to estimate spatial distribution of snow accumulation in the Samudra Tapu glacier (the Great Himalayan Range), Western Himalaya, India. An impulse radar system with 350 MHz antenna was mounted on a helicopter for the estimation of snow depth. The dielectric properties of snow were measured at a representative site (Patseo Observatory) using a snow fork to calibrate GPR data. The snow depths estimated from GPR signal were found to be in good agreement with those measured on ground with an absolute error of 0.04 m. The GPR survey was conducted over Samudra Tapu glacier in March 2009 and 2010. A kriging-based geostatistical interpolation method was used to generate a spatial snow accumulation map of the glacier with the GPR-collected data. The average accumulated snow depth and snow water equivalent (SWE) for a part of the glacier were found to be 2.23 m and 0.624 m for 2009 and 2.06 m and 0.496 m for 2010 respectively. Further, the snow accumulation data were analysed with various topographical parameters such as altitude, aspect and slope. The accumulated snow depth showed good correlation with altitude, having correlation coefficient varying between 0.57 and 0.84 for different parts of the glacier. Higher snow accumulation was observed in the north- and east-facing regions, and decrease in snow accumulation was found with an increase in the slope of the glacier. Thus, in this study we generate snow accumulation/SWE information using airborne GPR in the Himalayan terrain.

Keywords

Glacier, Ground Penetrating Radar, Snow Accumulation, Snow Water Equivalent.

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B. P. Chandra ¹, V. Sinha ¹, H. Hakkim ¹, A. Kumar ¹, H. Pawar ¹, A. K. Mishra ¹, G. Sharma ¹, Pallavi ¹, S. Garg ¹, Sachin D. Ghude ², D. M. Chate ², Prakash Pithani ², Rachana Kulkarni ², R. K. Jenamani ³, M. Rajeevan ⁴

Affiliations
1 Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Mohali, Sector 81, S.A.S. Nagar, Manauli PO 140 306, IN
2 Indian Institute of Tropical Meteorology, Pashan, Pune 411 008, IN
3 India Meteorological Department, New Delhi 110 003, IN
4 Ministry of Earth Sciences, Government of India, New Delhi 110 003, IN

Abstract

We studied the impact of the odd–even traffic rule (implemented in Delhi during 1–15 January 2016) on primary traffic emissions using measurements of 13 volatile organic compounds, carbon monoxide, carbon dioxide and methane at a strategic arterial road in Delhi (28.57°N, 77.11°E, 220 m amsl). Whole air samples (n = 27) were collected during the odd–even rule active (OA) and inactive (OI) days, and analysed at the IISER Mohali Atmospheric Chemistry Facility. The average mass concentration ranking and toluene/benzene ratio were characteristic of primary traffic emissions in both OA and OI samples, with the largest fraction comprising aromatic compounds (55– 70% of total). Statistical tests showed likely increase (p ≤ 0.16; OA > OI) in median concentration of 13 out of 16 measured gases during morning and afternoon periods (sampling hours: 07 : 00–08 : 00 and 13 : 30–14 : 30 IST), whereas no significant difference was observed for evening samples (sampling hour: 19 : 00–20 : 00 IST). This suggests that many four-wheeler users chose to commute earlier, to beat the 8 : 00 AM–8 : 00 PM restrictions, and/or there was an increase in the number of exempted public transport vehicles. Thus, the odd–even rule did not result in anticipated traffic emission reductions in January 2016, likely due to the changed temporal and fleet emission behaviour triggered in response to the regulation.

Keywords

Odd–Even Rule, Pollution, PTR-MS, Traffic, VOCs.

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A. Kumar ¹, A. Sarangi ²

Affiliations
1 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN

Abstract

Judicious use of water plays a vital role in enhancing its productivity in agriculture. In India, surface irrigation covers about 88% of the irrigated area with application efficiency ranging from 30% to 40%. Therefore, it becomes imperative to improve water application efficiency of canal commands and other areas under surface irrigation. Water application efficiency can be improved by minimizing conveyance losses and by judicious irrigation scheduling pertaining to different crops, which can be accomplished by accurate measurement of irrigation water. Measurement of irrigation water supplied to farmlands not only assists in the saving of water but also enhances water productivity in agriculture. The most popular device for measuring irrigation water in field channels is the Parshall flume, which has undergone a series of modifications to simplify its construction, improve the accuracy of measurements and reduce its cost leading to its wider acceptance by the stakeholders. Thus, it becomes imperative to deve­lop an accurate, low-cost and portable flow-measuring device for enhancing agricultural water productivity. Moreover, a review of the literature reveals limited availability of portable and digital flow-measuring devices for real-time measurement of surface irrigation through field channels. Nonetheless, it is established that the use of flow-measuring devices in surface irrigation will not only save water but also expand the area under irrigation, ensure its sustainability and improve agricultural water productivity.

Keywords

Agriculture, field channels, flow-measuring devices, surface irrigation, water productivity.

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K. K. Yadav ¹, N. Chouhan ², R. Thubstan ², S. Norlha ², J. Hariharan ², C. Borwankar ², P. Chandra ², V. K. Dhar ¹, N. Mankuzhyil ², S. Godambe ², M. Sharma ², K. Venugopal ², K. K. Singh ¹, N. Bhatt ², S. Bhattacharyya ¹, K. Chanchalani ², M. P. Das ², B. Ghosal ², S. Godiyal ², M. Khurana ², S. V. Kotwal ², M. K. Koul ², N. Kumar ², C. P. Kushwaha ², K. Nand ², A. Pathania ², S. Sahayanathan ¹, D. Sarkar ², A. Tolamati ², R. Koul ³, R. C. Rannot ⁴, A. K. Tickoo ⁵, V. R. Chitnis ⁶, A. Behere ⁷, S. Padmini ⁷, A. Manna ⁷, S. Joy ⁷, P. M. Nair ⁷, K. P. Jha ⁷, S. Moitra ⁷, S. Neema ⁷, S. Srivastava ⁷, M. Punna ⁷, S. Mohanan ⁷, S. S. Sikder ⁷, A. Jain ⁷, S. Banerjee ⁷, Krati ⁷, J. Deshpande ⁷, V. Sanadhya ⁸, G. Andrew ⁸, M. B. Patil ⁸, V. K. Goyal ⁸, N. Gupta ⁸, H. Balakrishna ⁸, A. Agrawal ⁸, S. P. Srivastava ⁹, K. N. Karn ⁹, P. I. Hadgali ⁹, S. Bhatt ⁹, V. K. Mishra ⁹, P. K. Biswas ⁹, R. K Gupta ⁹, A. Kumar ⁹, S. G. Thul ⁹, R. Kalmady ¹⁰, D. D. Sonvane ¹⁰, V. Kumar ¹⁰, U. K. Gaur ¹⁰, J. Chattopadhyay ¹¹, S. K. Gupta ¹¹, A. R. Kiran ¹¹, Y. Parulekar ¹¹, M. K. Agrawal ¹¹, R. M. Parmar ¹¹, G. R. Reddy ¹², Y. S. Mayya ¹³, C. K. Pithawa ¹⁴

Affiliations
1 Astrophysical Sciences Division, Bhabha Atomic Research Centre, Mumbai 400 085, India; Homi Bhabha National Institute, Mumbai 400 085, India, IN
2 Astrophysical Sciences Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
3 Formerly at Astrophysical Sciences Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
4 Raja Ramanna Fellow at Astrophysical Sciences Division, Mumbai 400 085, India, IN
5 Deceased, IN
6 Department of High Energy Physics, Tata Institute of Fundamental Research, Mumbai 400 005, India, IN
7 Electronics Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
8 Control and Instrumentation Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
9 Center for Design and Manufacture, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
10 Computer Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
11 Reactor Safety Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
12 Formerly at Reactor Safety Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
13 Formerly at Reactor Control Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN
14 Formerly at Electronics Division, Bhabha Atomic Research Centre, Mumbai 400 085, India, IN

Abstract

The MACE telescope has recently been commissioned at Hanle, Ladakh, India. It had its first light in April 2021 with a successful detection of very high energy gamma-ray photons from the standard candle Crab Nebula. Equipped with a large light collector of 21 m diameter and situated at an altitude of ~4.3 km amsl, the MACE telescope is expected to explore the mysteries of the non-thermal Universe in the energy range above 20 GeV with very high sensitivity. It can also play an important role in carrying out multi-messenger astronomy in India.

Keywords

Gamma-ray astronomy, high energy radiative processes, non-thermal Universe, telescope.

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Y. Srivastava ¹, A. Kumar ¹, A. Basu Sarbadhikari ¹, D. Ray ¹, V. M. Nair ¹, A. Das ¹, A. D. Shukla ¹, S. Sathiyaseelan ¹, R. Ramachandran ¹, B. Sivaraman ¹, S. Vijayan ¹, N. Panwar ¹, A. J. Verma ¹, N. Srivastava ¹, A. Rani ¹, G. Arora ¹, R. R. Mahajan ¹, A. Bhardwaj ¹

Affiliations
1 Physical Research Laboratory, Ahmedabad 380 009, India., IN

Abstract

No Abstract.

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