Current Science

Ratan Kar ¹, R. S. Singh ¹

Affiliations
1 Birbal Sahni Institute of Palaeobotany, 53 University Road, Lucknow 226 007, IN

Abstract

No Abstract.

Full Text

   

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.

Full Text

   

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.

Full Text

   

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.

Full Text

   

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.

Full Text

   

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.

Full Text

   

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.

Full Text

   

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.

Full Text

   

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.

Full Text

   

Alok Kumar Srivastava ¹, Kulandaivelu Velmourougane ², T. Bhattacharyya ³, D. Sarkar ³, D. K. Pal ⁴, J. Prasad ⁴, G. S. Sidhu ⁵, K. M. Nair ⁶, A. K. Sahoo ⁷, T. H. Das ⁷, R. S. Singh ⁸, 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 ⁷, A. K. Singh ⁸, S. K. Reza ³, D. Dutta ⁷, C. Mandal ³, D. K. Mandal ³, S. Srinivas ³, P. Tiwary ³, K. Karthikeyan ³, M. V. Venugopalan ², Mausumi Raychaudhuri ⁹, D. K. Kundu ⁹, K. G. Mandal ⁹, Ashutosh Kumar ¹, G. Kar ⁹, S. L. Durge ³, 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 ⁶, Sujatha ⁶, B. M. N. Reddy ⁶, P. Sreekumar ⁶, D. P. Dutta ¹⁰, L. Gogoi ¹⁰, V. N. Parhad ³, A. S. Halder ⁷, R. Basu ⁷, R. Singh ⁸, B. L. Jat ⁸, D. L. Oad ⁸, N. R. Ola ⁸, 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 ², S. Mahapatra ¹⁰, S. Puspamitra ¹⁰, N. Gautam ³, B. A. Telpande ³, A. M. Nimje ³, C. Likhar ³, S. Thakre ³

Affiliations
1 National Bureau of Agriculturally Important Microorganisms, Mau 275 101, IN
2 Central Institute for Cotton Research, Nagpur 440 010, 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 Directorate of Water Management, Bhubaneswar 751 023, IN
10 Regional Centre, National Bureau of Soil Survey and Land Use Planning, Jorhat 785 004, IN

Abstract

Comprehensive reports on land-use changes and their impact on soil biological properties, specifically microbial population in the Indo-Gangetic Plains (IGP) of India, are lacking. Since IGP is the most fertile land, data on microbial population of IGP may contribute towards the evaluation of various soil quality parameters, disease suppression, organic matter decomposition, plant growth promotion and soil management pattern. To enhance our knowledge on culturable microbial populations in different soil horizons of the agro-ecological sub-regions (AESRs) in the IGP, a study has been undertaken to collect soil samples from the established benchmark (BM) spots of these plains with an objective to investigate the impacts of bioclimates, soil depth, cropping systems, land use systems and management practices on the distribution of culturable microbial population. Bacterial : fungal ratios are significantly different across the land use types. The bacterial and fungal populations are strongly and negatively correlated with soil depth and maximum microbial population (40%) exists in the surface horizon (0-30 cm) than in the subsurface horizon (121-150 cm). Generally, bacterial populations are higher than actinomycetes and fungal populations in all soil profiles of the IGP. Approximately 10% decrease in Shannon diversity index has been observed with increase of 30 cm depth and 89% fall between surface and subsurface profiles. Non-significant difference in microbial population (P < 0.05) is noticed across the management and land use systems. Sub-humid (moist) bioclimatic system recorded higher microbial population than sub-humid (dry) and semi-arid bioclimatic systems. Legume-based cropping system has higher microbial population than cereal or vegetable-based cropping.

Keywords

Agro-Ecosystems, Microbial Population, Land Use Type, Soil Depth.

Full Text

   

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.

Full Text

   

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.

Full Text

   

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.

Full Text

   

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.

Full Text

   

P. K. Singh ¹, K. K. Singh ¹, L. S. Rathore ¹, A. K. Baxla ¹, S. C. Bhan ¹, Akhilesh Gupta ², G. B. Gohain ¹, R. Balasubramanian ³, R. S. Singh ⁴, R. K. Mall ⁴

Affiliations
1 Agromet Service Cell, India Meteorological Department, Lodhi Road, New Delhi 110 003, IN
2 Deparment of Science and Technology, New Delhi 110 016, IN
3 Agrimet Pune, New Delhi 411 005, IN
4 Banaras Hindu University, Varanasi 221 005, IN

Abstract

The CERES (crop estimation through resource and Environment Synthesis)-rice model incorporated in DSSAT version 4.5 was calibrated for genetic coefficients of rice cultivars by conducting field experiments during the kharif season at Jorhat, Kalyani, Ranchi and Bhagalpur, the results of which were used to estimate the gap in rice yield. The trend of potential yield was found to be positive and with a rate of change of 26, 36.9, 57.6 and 3.7 kg ha^-1 year^-1 at Jorhat, Kalyani, Ranchi and Bhagalpur districts respectively. Delayed sowing in these districts resulted in a decrease in rice yield to the tune of 35.3, 1.9, 48.6 and 17.1 kg ha^-1 day^-1 respectively. Finding reveals that DSSAT crop simulation model is an effective tool for decision support system. Estimation of yield gap based on the past crop data and subsequent adjustment of appropriate sowing window may help to obtain the potential yields.

Keywords

Agroclimatic Zones, Genetic Coefficients, Rice Model, Yield Gap.

Full Text

   

P. K. Singh ¹, K. K. Singh ¹, K. K. Gill ², Ram Niwas ³, R. S. Singh ⁴, Sanjay Sharma ⁵

Affiliations
1 Agromet Service Cell, India Meteorological Department, New Delhi 110 003, IN
2 School of Climate Change and Agri Meteorology, Punjab Agricultural University, Ludhiana 141 004, IN
3 Department of Agri Meteorology, CCSHAU, Hisar 125 004, IN
4 Department of Geophysics, Banaras Hindu University, Varanasi 221 005, IN
5 Department of Soil Science & Chemistry, College of Agriculture, Indore 452 001, IN

Abstract

The CERES-wheat crop growth simulation model has been calibrated and evaluated for two wheat cultivars (PBW 343 and PBW 542) for three sowing dates (30 October, 15 November and 30 November) during 2008-09 and 2009-10 to study partitioning of leaf, stem and grains at Ludhiana, Punjab, India. The experimental data and simulated model data were analysed on partitioning of leaf, stem and grains, and validated. It was found that the model closely simulated the field data from phenological events and biomass. Simulated biological and grain yield was in accordance with-field experiment crop yield within the acceptable range. The correlation coefficient between field-experiment and simulated yield data and biomass data varied significantly from 0.81 and 0.96. The model showed overestimation from field-experimental yield for both cultivars. The cultivar PBW 343 gave higher yield than cultivar PBW 542 on 15 November during both years. The model performance was evaluated and it was found that CERES-wheat model could predict growth parameters like days to anthesis and maturity, biomass and yield with reasonably good accuracy (error less than 8%) and also correlation coefficient between field-experimental and simulated yield data and biomass data varied from 0.94 and 0.95. The results showed that the correlation coefficient between simulated and districts yield varied from 0.41 to 0.78 and also significantly at all six selected districts. The results may be used to improve and evaluate the current practices of crop management at different growth stages of the crop to achieve better production potential.

Keywords

Biomass Partitioning, Genetic Coefficient, Phenology Stages, Soil Parameters.

Full Text

   

V. S. Santhosh Mithra ¹, Raji Pushpalatha ¹, S. Sunitha ¹, James George ¹, P. P. Singh ², R. S. Singh ², J. Tarafdar ³, Surajit Mitra ³, Chandra Deo ⁴, Sunil Pareek ⁵, B. K. M. Lakshmi ⁶, R. Shiny ¹, G. Byju ¹

Affiliations
1 ICAR-Central Tuber Crops Research Institute, Thiruvananthapuram 695 017, IN
2 Rajendra Agricultural University, Pusa, Samasthipur 848 125, IN
3 Bidhan Chandra Krishi Vishwavidyalaya (BCKV), Kalyani 741 252, IN
4 Narendra Deva University of Agriculture and Technology, Faizabad 224 229, IN
5 Maharana Pratap University of Agriculture and Technology, Udaipur 313 001, IN
6 Shri Konda Laxman Telangana State Horticultural University, Rajendra Nagar 500 030, IN

Abstract

A study was conducted to evaluate the wider applicability of sweet potato growth model, ‘SPOTCOMS’ for simulating the phenology and yield over a set of agroclimatic conditions in India. The model simulated the phenology of the crop as a function of growing degree days. The genetic coefficients required for the model were estimated from the field experiments conducted with sweet potato variety, Sree Bhadra and other local varieties at the study locations. The model simulated the yield of the sweet potato well and the statistical indices calculated between the simulated and observed yields stated the reliability of the model simulations. The agreement index (D-index) for Sree Bhadra ranged from 0.55 to 0.99, and the D-index for local varieties ranged from 0.51 to 1.00. The calculated values of normalized objective function ranged from 0.01 to 0.10 for Sree Bhadra and 0.00 to 0.22 for other local varieties, and indicated better agreement of simulated and observed yields. The normalized ischolar_main mean square error ranged from 0.80% to 10.40% for Sree Bhadra and 0.00% to 22.44% for other varieties, and these results suggested the wider applicability of the model with excellent to good simulations. The model also simulated dry matter distribution in tubers pertaining to different stresses such as water, nitrogen and potassium. The study revealed that the simulation model ‘SPOTCOMS’ can be used for simulating the yield as well as to manage the stresses during the crop growth period and to optimize best management practices for the crop cultivation irrespective of the agroclimatic conditions.

Keywords

Crop Phenology, Calibration, Growing Degree Days, SPOTCOMS, Simulation.

Full Text

   

Mukunda M. Gogoi ¹, S. Suresh Babu ¹, B. S. Arun ¹, K. Krishna Moorthy ², A. Ajay ³, P. Ajay ⁴, Arun Suryavanshi ⁵, Arup Borgohain ⁶, Anirban Guha ⁷, Atiba Shaikh ⁸, Binita Pathak ⁴, Biswadip Gharai ⁹, Boopathy Ramasamy ¹⁰, G. Balakrishnaiah ¹¹, Harilal B. Menon ⁸, Jagdish Chandra Kuniyal ¹², Jayabala Krishnan ¹³, K. Rama Gopal ¹¹, M. Maheswari ¹³, Manish Naja ¹⁴, Parminder Kaur ⁷, Pradip K. Bhuyan ⁴, Pratima Gupta ¹⁵, Prayagraj Singh ¹⁶, Priyanka Srivastava ¹⁴, R. S. Singh ¹⁷, Ranjit Kumar ¹⁵, Shantanu Rastogi ¹⁶, Shyam Sundar Kundu ⁶, Sobhan Kumar Kompalli ¹, Subhasmita Panda ¹⁰, Tandule Chakradhar Rao ¹¹, Trupti Das ¹⁰, Yogesh Kant ¹⁸

Affiliations
1 Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram 695 022, IN
2 Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
4 Centre for Atmospheric Studies, Dibrugarh University, Dibrugarh 786 004, IN
5 Regional Remote Sensing Centre, Indian Space Research Organisation, Nagpur 440 033, IN
6 North Eastern – Space Application Centres, Shillong 793 103, IN
7 Department of Physics, Tripura University, Suryamaninagar, Agartala 799 022, IN
8 Department of Marine Sciences, Goa University, Goa 403 206, IN
9 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
10 Indian Institute of Mineral and Materials Technology, Bhubaneswar 751 013, IN
11 Sri Krishna Devaraya University, Anantapur 515 003, IN
12 G. B. Pant Institute of Himalayan Environment and Development, Kullu 175 126, IN
13 Tamil Nadu Agricultural University, Coimbatore 641 003, IN
14 Aryabhatta Research Institute of Observational Sciences, Nainital 263 002, IN
15 Department of Chemistry, Dayalbagh Educational Institute, Agra 282 005, IN
16 Department of Physics, D.D.U. Gorakhpur University, Gorakhpur 273 009, IN
17 Department of Chemical Engineering, IIT-BHU, Varanasi 221 005, IN
18 Indian Institute of Remote Sensing, Indian Space Research Organisation, Dehradun 248 001, IN

Abstract

In this study, we assess the response of ambient aero-sol black carbon (BC) mass concentrations and spec-tral absorption properties across Indian mainland during the nation-wide lockdown (LD) in connection with the Coronavirus Disease 19 (COVID-19) pan-demic. The LD had brought near to total cut-off of emissions from industrial, traffic (road, railways, ma-rine and air) and energy sectors, though the domestic emissions remained fairly unaltered. This provided a unique opportunity to delineate the impact of fossil fuel combustion sources on atmospheric BC characte-ristics. In this context, the primary data of BC meas-ured at the national network of aerosol observatories (ARFINET) under ISRO-GBP are examined to assess the response to the seizure of emissions over distinct geographic parts of the country. Results indicate that average BC concentrations over the Indian mainland are curbed down significantly (10–40%) from pre-lockdown observations during the first and most in-tense phase of lockdown. This decline is significant with respect to the long-term (2015–2019) averaged (climatological mean) values. The drop in BC is most pronounced over the Indo-Gangetic Plain (>60%) and north-eastern India (>30%) during the second phase of lockdown, while significant reduction is seen during LD1 (16–60%) over central and peninsular Indian as well as Himalayan and sub-Himalayan regions. De-spite such a large reduction, the absolute magnitude of BC remained higher over the IGP and north-eastern sites compared to other parts of India. Notably, the spectral absorption index of aerosols changed very little over most of the locations, indicating the still persisting contribution of fossil-fuel emissions over most of the locations.

Keywords

ARFINET, Black Carbon, COVID-19.

Full Text