2. RNPS 25 Identifying systems for carbon sequestration and increased productivity in semi-arid tropical environments

3. Collaborating Scientists within Institutions Collaborating Scientists within Institutions Principal Investigator Dr. S.P.Wani ICRISAT Co-PI Dr. T.J. Rego Dr. K.P.C. Rao Mr. P. Pathak Dr. Piara Singh NBSS & LUP Dr. Tapas Bhattacharyya Dr. P. Chandran DR. S.K. Ray Dr. (Mrs.) C. Mandal Dr. D.K. Pal Dr. M.V. Venugopalan Mr. S.L. Durge Dr. P. Srivastava CRIDA Dr. V. Ramesh Dr. K.L. Sharma DR. M. Vanaja IISS Dr. M.C. Manna Dr. K. Bandyopadhyay DR. T.R. Rupa

4. Objectives  Identify the potential carbon sequestration production systems in the SAT benchmark sites and establish relationship amongst different factors with the carbon stock and systems productivity.  Evaluate and validate the existing simulation models for predicting the performance of different systems for carbon sequestration.

5. NBSS & LUP Soil sample collection & distribution from selected BM spots Site characteristics & information on soils & management §Mineralogy of clay + silt §Smectite quantity §Bulk density §Particle size density §Structure §HC, COLE §pH, EC,CEC §Organic C §Extractable Cations §Inorganic C §CaCO 3 §CO 2 Clay IISS Soil Analysis §WSA §WSC §WS Carbon §POM §Passive pools §DHA §Available P CRIDA §Available S §Total P reading §Total S reading §Meteorological §Agronomic §Infiltration §Respiration ICRISAT §WHC §Organic C §Total P §SMBC §SMBN §Respiration §Net N mineralisation §Total & Avail. N §Plant C & N Responsibilities of participating institutions Modeling work

6. Progress §Benchmark sites identified (20 sites covering 25 spots) §Site characteristics and information on soils and management practices collected for each site §Soil samples collected (horizon wise) for analysis of physico-chemical and biological properties

7. Benchmark sites in SAT Name of DistrictNo. of Spots 1. Nagpur 3 2. Akola 1 3. Amravati 1 4. Nasik 1 5. Medak 2 6. Solapur 1 7. Rajkot 2 8. Rangareddy 1 9. Mehaboobnagar 2 10. Bellary 1

8. Benchmark Sites in SAT (contd.) 11. Bangalore1 12. Bhopal1 13. Jabalpur1 14. Indore1 15. Bidar1 16. Coimbatore1 17. Tuthukudi1 18. Umeria1 19. Dindori1 20. Kota1 Total25

9. Grouping of BM Spots under different bio-climate  SH(M): DADARGHUGRI, KARKELI, KHERI  SH(D): LINGA, PANJRI, NABIBAGH, BORIPANI  SA(M): SAROL, ASRA, VIJAYPURA, JHALIPURA, BHATUMBRA  SA(D): PARAL, KOVILPATTY, PALATHURAI, SEMLA, SOKHDA, KAUKUNTLA, JAJAPUR,HAYATNAGAR, KASIREDDIPALLI, PATANCHERU, KONHERI, KALWAN  A : TELIGI

10.  Systems:  Horticultural ( Orange) (HS)  Agricultural ( Soybean-Wheat/Gram) (AS)  Managements HM: High Management FM: Farmers’ Management LM: Low Management Systems and Managements LINGA BM Spot 1

11. LINGA SERIES HS (Orange)--LM Pedon-2

12. AS : Cotton-Pigeonpea/ Sorghum(LM) PARAL SERIES

13. Distribution of BD with depth Sub-humid (dry) BD is higher in FM It is lowest in HM It is higher in HS

14. Sub-humid (dry) HC shows the trend HM < FM <LM This trend indicates indirect relation with SOC content Relatively high pH and E.S.P. support low HC in HM Distribution of H.C. with depth

15. Mean weight diameter (MWD)  Ecosystems: Forest system > Agricultural & Horticultural system  Boripani series(Forest ecosystem) in highest range (4.58 –5.96 mm)  Farmers management > Low & High management practices  Management practices: FM > LM & HM  Black soils Linga, Asra & Paral < Panjri, Nabibagh, Sarol & Kovilpatti  Red soils  Vijayapura & Palathurai series as high as Panjri, Nabibagh, Sarol & Kovilpatti

16. Moisture retention characteristics  Highest moisture retention in Asra (0.480 g g -1 at 0.33 bar) and Linga (0.447 g g -1 at 0.33 bar) series of Maharashtra  Lowest moisture retention in Palathurai series (0.037 g g -1 at 15 bars) TN < Vijayapura series (0.041 g g -1 at 15 bars), Karnataka.  Moisture retention increased with increase in depth (exception Palathurai series reduced after 46-95 cm depth.  Black soil series: HM > FM  Red soil series (Vijayapura series): FM>HM

17. Total N (ppm) in different cropping systems of different soil series in India

18. Total Phosporous (ppm) in different cropping systems of different soil series in India

19. Nagpur Forest 0100200300400500600700 0-16 16-44 44-57 57-94 Soil depth Tot P Reserve forest

20. Distribution of Available Sulphur (ppm) in the soil profiles of LINGA Series( Sub-humid dry) at Nagpur

21. Distribution of available Sulphur (ppm) in the soil profiles of ASRA series(semi-arid moist), Amravati (MS)

22. Distribution of available Sulphur in the soil profile of Kovilpatti series(Arid) at Tuthukudi (TN)

23. Distribution of SOC with Depth §SOC decreases with depth §SOC decreases in the order LM>FM>HM §SOC in LM is higher throughout the profile even at greater depth Sub-humid (dry) Organic Carbon in different cropping system for different soil series in India

24. Distribution of SOC with Depth in Asra Series SOC in FM(org) and FM show similar decreasing trend FM (org) maitains higher SOC even in greater depth HM registers much higher SOC even at greater depth due to addition of sunhemp in the crop rotation Semi-arid (moist)

25. FULVIC ACID C

26. Humic acid C

27. Microbial biomass-C (mg C kg -1 soil) for different cropping systems in different locations of India Amaravathi Asra 0100200300400 0-14 40-59 91-150 Microbial Biomass-C Cotton/Ppea-FMSoy/Ppea-FMSoy-Gram-FM Sss

29. Microbial biomass-N (mg N kg -1 soil) in different cropping system for different soil series in India

31. SYSTEM INTERACTION Sub-humid (dry) Soil parameters ASHS FMHMLM SOCHigh (surface)Lowhigher BDHigherLowest- SICLow (0.6-0.8)High(0.8-1.3)High(0.8-1.1) HCmediumLowesthigh CECHigh ESP0.4-0.60.5-2.00.5-1.0 pH7.7-7.97.8-8.07.8-7.9

32. Modeling Work  Identified CENTURY model to assess carbon stocks at all locations targeted by this project  APSIM and DSSAT models are the crop growth models to assess the changes in soil productivity associated with changes in soil carbon  Work on calibration of century model initiated

33. Why century model to assess C stocks?  Ability to model a diverse array of ecosystems  Capability to simulate a wide range of land use and management options  Possibility to alter the rates of various processes using observed data  Extensive use and testing around the world on a diverse array of systems  User friendly front end.

34. Reasons for Choosing APSIM Model  Soil is central to the model & changes in it due to cropping and management are accumulated and carried forward  Soil N and Residue modules which simulates the changes in SOM represent the process adequately  Its plug-in & pull out approach  ICRISAT has necessary expertise to assist in use of APSIM

35. Simulation of Soil Organic pools in DSSAT A new input file for all the crop models has been implemented in DSSAT version 3.5. It is named as SOILN 980.SOL. Many of the Co-efficients involved in simulating the decomposition of soil organic matter have been externalized. This allows uses to specify their own coefficients for simulating long-term simulation.

36. Student’s work Objectives are To identify the potential cropping systems and appropriate management practices for carbon sequestration at selected 11 benchmark spots(Long term experiment sites) in the semi-arid environments of India To evaluate and validate existing simulation models for predicting the performance of different systems for carbon sequestration in semi-arid tropics Work is in progress

37.  Cooperating centers have done exceeding well for all the BM sites.  Recently held review and planning meeting decided to add 3 BM spots to cover varying conditions in the eco- regions.  Difficult task of analyzing and archiving the data is ahead of us which we are confident to do it ahead of plan. Conclusions

38.  All datasets will be assembled and compiled centrally following Aridity Index  Website has been launched  Selected datasets put on the websites  Analysis & relation between various parameters and calculation of various stocks and pools will be undertaken Looking Forward

39. Looking Forward (contd.)  Analysis of long term experiment sites by student will result in identifying suitable C sequestration systems in SAT.  Simulation C modeling using appropriate models will be enable to extrapolate the results from BM sites to eco-regional level.

43. Thank you.