Assessing mitigation potential of existing agroforestry systems in g g p g g y ysome districts of Indo‐gangetic plains (IGP) in India

Ajit,  S.K.Dhyani, Ramnewaj, A.K.Handa, Badre Alam, Rajender Prasad, R.H.Rizvi, Amit Jain and Uma

National Research Centre for Agroforestry (NRCAF), (Indian Council of Agricultural Research)

Gwalior Road Near Pahuj DamGwalior Road, Near Pahuj DamJHANSI‐284003 (UP), India

Corresponding Author: AJIT , Principal Scientist, NRCAF, Jhansie‐mail:  umaajitgupta123@gmail.com,      umaajitgupta@yahoo.co.inOffice(O):0510‐2730213/2730214     FAX: 0510‐2730364   Mobile: 09415092880

About the Project

• This study on 

Mitigation potential of existing agroforestry systems on farmers field

was initiated in 2011 under the NICRA Project (N ti l I iti ti Cli t R ili t A i lt )(National Initiative on Climate Resilient Agriculture), launched by the Indian Council of Agricultural Research (ICAR), Ministry of Agriculture, Government of India, New Delhi (www.nicra‐icar.in). 

• The basic objective of this study wasThe basic objective of this study was 

to simulate the CSP of existing AFS on farmer’s fieldfarmer s field 

For simulating the carbon sequestration potential of existing agroforestry systems in various districts of IGP we have used CO2FIX model

CO2FIX model •The CO2FIX model V 3.1 was developed at CentroAgronómico Tropical de Investigación y Enseñenza

systems in various districts of IGP , we have used CO2FIX model

Agronómico Tropical de Investigación y Enseñenza(CATIE), Wageningen, Netherland under theCASFOR II (Carbon Sequestration in ForestedLandscapes) project.p ) p j•CASFOR II was financed through the EuropeanCommission INCO2-programme. Additional fundingwas received from the Dutch Ministry ofwas received from the Dutch Ministry ofAgriculture, Nature Management and Fisheries underthe North-South programme, and by the MexicanNational Council of Science and Technology.gy

•The software can be downloaded free of cost from site http://www.efi.fi/projects/casfor

Why CO2FIX• Ravindranath and Ostwald (2008) have compiled and compared different modelsRavindranath and Ostwald (2008) have compiled and compared different models 

used in estimating changes in carbon stock for forestry and plantation projects. 

• CO2FIX was preferred over others (viz PROCOMAP, CENTURY and ROTH) for the present study since only CO2FIX can simulate the carbon dynamics of single /multiple species simultaneously, and can handle trees with varied ages and agroforestry systems (AFS). 

• Moreover, CO2FIX outputs the biomass and C separately in above and below d t t h t i (i i i ) i dditi t il bground tree components cohorts wise (i.e species wise) in addition to soil carbon

dynamics. 

• In this study, we are estimating the carbon sequestration potential of existing agroforestry systems at farmers’ fields in different district of Indogangetic plainsagroforestry systems at farmers  fields in different district of Indogangetic plains and it was anticipated to observe varying tree species existing at farmers’ fields. Accordingly CO2FIX was more appropriate to handle multiple species simultaneously in addition to field crops. 

• Nair et al. (2005) has also mentioned that CO2FIX is a user friendly model for dynamically estimating the carbon sequestration potential of forest management and afforestation project and is readily adaptable for agroforestry.

CO2FIX in nut‐shell

• In CO2FIX model, the biomass and carbon credits are simulated at the hectare scale with time steps of one yearat the hectare scale with time steps of one year. 

• The biomass module converts volumetric net annual increment data to the annual carbon stock of the biomass compartment. 

• Turnover and harvest parameter drive the fluxes from biomass to soil. The model has a soil module known as YASOO, which takes into account the initial litter quality and the effect of climate oninto account the initial litter quality and the effect of climate on decomposition. Litter enters the soil module based on the size of the litter and is then dissociated into contents of different classes f i d Th lidit f it il b ti tof organic compounds. The validity of its soil carbon estimates, 

mass loss estimates and ability to appropriately describe the effects of climate on decomposition rates has been tested within a wide range of environments. 

Input Parameters required for the model

• The main input parameters relevant to CO2FIX model are the cohort wise values for the 

• stem‐CAI (current annual increment in m3 ha‐1 yr‐1) over years;years; 

• relative growth of the foliage, branches, leaf and root with respect to the stem growth over years;

• turnover rates for foliage, branches  and roots; 

• and climate data of the site ( annual precipitation in mm and monthly values of minimum and maximum temperatures in 0C )monthly values of minimum and maximum  temperatures in 0C ). 

• Other inputs to the model includes initial surface soil organic carbon (Mg C ha‐1), rotation length for the tree species, per cent carbon contents  in different tree parts wood density and initial values of baseline carbon (Mgdifferent tree parts, wood density and initial values of baseline carbon (Mg C ha‐1) in different tree parts, when the simulation are being carried out for the existing tree plantations as in the present case.

The CO2FIX modules considered are:• BiomassMethodology adopted

• These Modules requires primary as well as secondary

Biomass • Soil and • Carbon accounting modules.

Methodology  adopted

q p y ydata on tree and crop components (called ‘cohorts’ inCO2FIX terminology).

District Level Data

Primary data

1. Name of existing tree species under agroforestry systems

Secondary data

1. Total Area of District (in ha).2. i) Agril. Land (in ha), ii) Non Agril. Land (in ha) iii)Forest Land (in ha) iv)Waste land g y y

2. Frequency of existing tree spp. under agroforestry system3. DBH of Maximum 10 plants (including smaller medium and large

(in ha), iii)Forest Land (in ha), iv)Waste land (in ha).3. Name and No. of blocks In District4. Area of blocks (in ha) and total no. of villages in district

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(including smaller, medium and large size tree ).

g5. Major crops with their productivity (q/ha) and Area under crop (in ha)6. District map(Block wise)7. Block map(village wise)

Tree Cohorts

• The tree species being grown on farmland were classified

Tree Cohorts

into three categories/cohort’s viz slow, medium and fastgrowing trees as per the nature of the tree species.

• DBH of the surveyed trees was used to approximately find• DBH of the surveyed trees was used to approximately findout the age of the standing trees. To derive the incrementaldata of tree stem growth, the volume equations published ing , q pState Forest Report-2009 were used as the secondary data.

• DBH (m) and stem volume (m3/tree) datasets were generatefor the species found in survey. The individual species wisegenerated data sets were then clubbed into single files forthe slow medium and fast growing species separatelythe slow, medium and fast growing species separately.

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• These three data sets pertaining to slow, medium and fastgrowing species were independently used to fit non-g g p p ylinear functions for stem volume-DBH relationships.These tree wise absolute stem volume-DBHrelationships were then converted into hectare wiserelationships were then converted into hectare wisestem volume-DBH relationships, by multiplying treewise stem volume from the average number of treesf d i ill i ifi dfound in a village in a specified category(slow/medium/fast).

• DBH was transformed back into age to obtainDBH was transformed back into age to obtainhectare wise stem volume–age relationships.Ultimately, these absolute stem volume values wereconverted into CAI (current annual increment inconverted into CAI (current annual increment inm3/ha/yr). Thus, we obtained the CAI equations forstem-volume-age for the three categories/cohorts ofslow, medium and fast growing trees in a given district.

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Crop Cohort• In order to simulate the crop component, the crop was considered as a ‘tree’ 

with a very small stem volume, no branches and a lot of foliage and roots. 

• The stem part is needed since allocation to foliage and roots are driven by• The stem part is needed, since allocation to foliage and roots are driven by stem increment. In order to keep the influence of the stem compartment as small as possible, a very small increment was specified, in our case 0.01 m3 ha‐1 yr‐1. y

• The foliage (grain and straw) and root compartment receive a very high relative increment (w.r.t. stem), say for example set as 8657 and 865 respectively for Ludhiana district. When the wood density has been set at p y y‘0.09’, the aboveground production is 8657*0.09*0.01 = 7.79 Mg DM ha‐1 (dry matter per hectare). Similarly, belowground production is 0.77 Mg DM ha‐1 for Ludhiana district.  

• Additionally, it was presumed for CO2FIX model that 5% of the above ground crop biomass (grain and straw) incorporates into the soil, while 95% is exported out from the system. Likewise, 30% of the below ground crop biomass is incorporated into the soil. 

Soil parameterization

• The district wise climatic data on monthly temperature and precipitation wasobtained from IMD (Indian Meteorological Department) and was fed as the

p

general parameters for the soil compartment of the model.

•The dynamic soil carbon model YASSO describes decomposition anddynamics of soil carbon in well drained soilsdynamics of soil carbon in well‐drained soils.

•The soil module consists of three litter compartments (non‐woody, coarse‐woody and fine‐woody) and five decomposition compartments (extractives,cellulose, lignin like compound, humus‐1 and humus‐2).

•Litter is produced in the biomass module through biomass turnover. For thesoil carbon module the litter is grouped as non‐woody litter (foliage and finesoil carbon module, the litter is grouped as non woody litter (foliage and fineroots), fine woody litter (branches and coarse roots) and coarse woody litter(stems and stumps).

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Statistical analysis of data:Statistical analysis of data: 

• The Statistical analysis of data has been done using SAS‐9.3 (SAS Institute’s Inc. @ 2011, Cary, North Carolina‐27513, USA). Carolina 75 3, USA).

• proc‐UNIVARIATE was used for computing the basic descriptive statistics and proc NLIN was used for fittingdescriptive statistics and proc‐NLIN was used for fitting the nonlinear equations to the data. 

• proc–SGPLOT was used for plotting the graphs to the observed and modeled biomass along with the residuals. 

/

Example of Ludhiana district

Site characteristics, dominant tree/crops and climatic data of the study area 

Attributes Ludhiana

Location 300 4’ N and 750 5’ E

Rainfall (mm), climate 681, Semiarid( ), ,

Soil type Sandy, Clayey Loam, Alkaline

Region Upper Gangetic plainsRegion Upper‐Gangetic plains

Dominant crops Rice, Wheat and Maize

Dominant agroforestry trees Populus  deltoides, Eucalyptus  tereticornis, 

Melia azedarach

Input parameter used in CO2FIX model for simulating tree biomass components in various tree cohorts (uniform for all three districts)

Cohorts Slow growing trees a

Medium growing trees b

Fast growing trees c

Rotation (year) 90 50 10Wood density (Mg DM/m3) 0.67 0.65 0.61Carbon content (% dry weight) 48 48 48Turnover rate foliage 0.5 0.5 0.6Turnover rate branch 0 02 0 04 0 02Turnover rate branch 0.02 0.04 0.02Turnover rate root 0.02 0.1 0.2Product allocation for Thinning harvesting*

Stem log wood 0.8 0.8 0.8

Stem slash 0.2 0.2 0.2

Branch log wood 0.8 0.8 0.2

Branch slash 0.2 0.2 0.8

Foliage slash 1 1 1Foliage slash soil 0.7 0.7 0.7

Estimated from a Negi (1984); Kumar et al. (2011);       b Jha (1995);        c Bargali et al. (1992);     *Haripriya (2001)

Cohorts Ludhiana

Primary survey results for the tree species observed in the district

Slow growing tree

Medium growing 

Fast growing 

Estimated averageEstimated average number of trees per hectare 

0.17 1.0 36.78

Estimated age of existing trees (years)  40 16 3

Observed average DBH of existing trees (cm)

29.2 25.28 7.5(cm)  

Slow Growing Medium Growing Fast Growing

Current Annual Increment (CAI) of the stem volume growth (m3 ha‐1yr‐1) over years for three tree cohorts 

Age CAI  Age CAI  Age CAI  Age CAI 

0 0.010 55 0.040 0 0.025 1.0 0.0005 0.010 60 0.047 5 0.028 2.0 0.00410 0.010 65 0.054 10 0.037 2.5 0.03115 0.012 70 0.060 15 0.053 3.0 0.24920 0.014 75 0.068 20 0.076 3.5 0.44725 0.016 80 0.072 25 0.106 4.0 0.67730 0.018 85 0.085 30 0.155 4.5 0.91235 0.022 90 0.091 35 0.217 5.0 1.31840 0.025 95 0.092 40 0.279 5.5 1.46945 0.030 100 0.087 45 0.292 6.0 1.58450 0.034 50 0.119 6.5 1.665

55 0.021 7.0 1.7407.5 1.7448.0 1.7318.5 1.7069.0 1.6289.5 1.581

Slow growing a Medium growing b Fast growing c

Foliage Age Rates Age Rates Age Rates

Relative growth of various tree components with respect to stem growth for tree cohorts (over years)

0 1 1 0.26 0 0.3010 0.50 5 0.63 2 0.4420 0.73 15 0.50 3 0.4030 0.64 20 0.38 4 0.3840 1.02 25 0.32 5 0.3740 1.02 25 0.32 5 0.3750 1.12 30 0.50 6 0.3260 0.98 7 0.5670 0.91 8 0.58

Branch Age Rates Age Rates Age Rates0 0 20 1 0 44 0 0 250 0.20 1 0.44 0 0.2510 0.18 5 0.44 2 0.2220 0.15 15 0.33 3 0.1830 0.16 20 0.38 4 0.1840 0.16 25 0.32 5 0.2150 0.15 30 0.32 6 0.2860 0.14 7 0.4370 0.14 8 0.58

Root Age Rates Age Rates Age Rates0 0 40 0 0 44 0 0 300 0.40 0 0.44 0 0.3010 0.40 5 0.48 2 0.4320 0.39 15 0.63 3 0.5830 0.30 20 0.60 4 0.4940 0.31 25 0.77 5 0.3650 0.31 30 0.82 6 0.3160 0.29 7 0.4770 0.27 8 0.37

Simulation results for the three districts of IGP’s at

Biomass accumulated in the tree/crop components and carbon sequestered under existing AFSthree districts of IGP’s at 

a glance

under existing AFS Sultanpur(6.14 trees/ha)

Ludhiana (37.95 trees/ha)

Dinajpur(6.20 trees/ha)

Tree Biomass (above and Baseline 2 56 2 88 2 45Tree Biomass (above and below ground ) Mg DM ha‐1

Baseline

Biomass

2.56 2.88 2.45

Simulated 8.24 4.67 8.22

Total Biomass (tree+ crop) Mg DM ha‐1

Baseline 11.14 25.97 12.10Simulated 17 05 28 41 17 59crop)  Mg DM ha 1 Simulated 17.05 28.41 17.59

Soil carbon(Mg C ha‐1)

Baseline

C b

8.13 9.12 8.16Simulated 8.63 24.51 9.28

Biomass carbon(Mg C ha‐1)

Baseline 4.92 11.21 5.33Simulated 7 75 12 45 8 00Carbon(Mg C ha 1) Simulated 7.75 12.45 8.00

Total carbon (biomass + soil)(Mg C ha‐1)

Baseline 13.05 20.43 13.49Simulated 16.38 36.96 17.28

Net carbon sequestered in agroforestryNet carbon sequestered in agroforestrysystems over the simulated period of thirtyyears(Mg C ha‐1) Carbon 

3.33 16.53 3.79

Estimated annual carbon sequestration sequesteredEstimated annual carbon sequestrationpotential of agroforestry system in differentdistricts of Indo‐Gangetic Plains

(Mg C ha‐1yr‐1)

0.111 0.551 0.126

Location Agroforestry System

Tree species  No. of tree per hectare

Age (year) CSP (Mg C ha‐1yr‐1)

References 

SBS Nagar, Punjab

Agrisilviculture P. deltoids 740 7 9.4 Chauhanet al. 2010

Dehradun,  Silviculture E. tereticornis 2500 3.5 4.4 DhyaniUttarakhand et al. 19962777* 2.5 5.9

Kurukkhetra, Haryana

Silvipasture A. nilotica 1250 7 2.81 Kauret al. 2002D. sissoo 1250 7 5.37

P juliflora 1250 7 6 5P. juliflora 1250 7 6.5Chandigarh Agrisilviculture L. 

leucocephala10666 6 10.48 Mittal and 

Singh 1989

Tripura** Silviculture T. grandis 444 20 3.32 Negiet al. 1990G. arborea 452 20 3.95

Tarai central d i i **

Silviculture T. grandis 570 10 3.74 Negildevision ** 

Uttarakhandet al. 1995500 20 2.25

494 30 2.87

Jhansi, Uttar Pradesh

Agrisilviculture A. procera 312 7 3.7 Ramnewajet al 2008Pradesh et al. 2008

Jhansi, Uttar Pradesh

Agrisilviculture A. pendula 1666 5.3 0.43 Raiet al. 2002

Location Agroforestry System

Tree species  No. of tree per hectare

Age (year) CSP (Mg C ha‐1yr‐1)

References 

Jhansi,Uttar Pradesh

Silviculture A. procera 312  10 1.79A. amara 312  10 1.00

Rai et al. 2000

A. indica 312  10 0.72A. pendula 312  10 0.95D. sissoo 312  10 2.55D. cinerea 312  10 1.05E officinalis 312 10 1 55E. officinalis 312  10 1.55E. tereticornis 312  10 0.81H. binata 312  10 0.58L. leucocephala 312  10 1.62M. azaderach 312  10 0.49T j 312 10 0 99T. arjuna 312  10 0.99

Hydarabad, Andhra Pradesh

Silviculture L. leucocephala 2500 9 10.32E. camaldulensis 2500 9 8.01D. sissoo 2500 9 11.47A. lebbeck 625 9 0.62

Rao et al. 2000

D. strictus 2500 9 0.58A. albida 1111 9 0.82A. tortilis 1111 9 0.39A. auriculiformis 2500 9 8.64A indica 625 9 0 72A. indica  625 9 0.72A. nilotica 1111 9 0.77T. indica  1111 9 0.43

Hydarabad, Andhra Pradesh

Agrisilviculture L. leucocephala 11111 4 2.77 Rao et al.  19916666 4 1.90

** hh h l l b dRaipur, ** Chhattisgarh Agrisilviculture G. arborea 592 5 3.23 Swami and Puri 2005

Coimbatore, ** Tamilnadu  Agrisilviculture C. equisetifolia 833 4 1.57 Viswanath et al. 2004

Kerala Home garden  Mixed tree spp. 667 71 1.60 Saha et al. 

Observed vs. CO2FIX simulated biomass for the independent validation data set alongwith the prediction bias curve (predbio: modelled biomass, obsbio:observed biomass,linfit: linear regression line fitted on modelled and observed biomass, resid:g f ,residual/error/bias in estimation)

Using paired t‐test for evaluating the significance of case‐wise differences betweenthe observed and CO2FIX modelled biomass for the validation data set (pred:modelled biomass obs:observed biomass)modelled biomass, obs:observed biomass)

The values of the t‐statistic comes out to be 2.14 with p‐value as 0.0679,to be 2.14 with p value as 0.0679, indicating thereby that the differences between the observed and the modelled biomass are not significant at 5% l l f i ifi5% level of significance. 

The average value of the percent bias in prediction was 5.66%.

Generalized stem volume equations are used forSimulating CSP of AFS using CO2FIX modelSimulating CSP of AFS using CO2FIX model

This rate of  decomposition is influenced by climatic factors viz temperature and rainfall

Simulating the effect 

of temp and rainfall onon 

Carbon Sequestered

NNAAHH KKNNAAHH KK

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