Dynamism of Agricultural Risk Drs. D. R. Reddy, Amor Ines, Sheshagiri Rao.

Dynamism of Agricultural Risk

Drs. D. R. Reddy, Amor Ines, Sheshagiri Rao

OverviewI. Decision Making Under Uncertainty

Risk Aversion and OptimizationExample of optimizing maize production in Kenya

II. Analyzing climate risks and risk management approaches at community/village level

Example 1: Srirangapura Village, MahabubnagarExample 2: groundnut in Anantapur

III. Using climate information to manage crop mixes: examples from Mahabubnagar

Decision Making Under Uncertainty

Amor Ines

Risks

Climatic outcome (e.g., rainfall)

Pro

bab

ility

den

sity

Risk Aversion

What would you rather do?

play?

no

yestails

heads0

2 y

y

Ex-Ante Impacts: Risk Aversion


play?

no

yestails

heads0

2 y

0.9 y



play?

no

yestails

heads0

2 y

0.8 y



play?

no

yestails

heads0

2 y

0.7 y


...the subjective value, under certainty,of the risky scenario or decision option

play?

no

yestails

heads0

2 yindifferent

certainty equivalent

Risk Aversion

E{w} w2w1

Returns, w

Risk Aversion

U(E{w})

U(w2)

U(w1)

E{w} w2w1

Returns, w

Risk Aversion

E{U(w)}

Returns, w

U(E{w})

U(w2)

U(w1)

E{w} w2w1

Risk Aversion

E{U(w)}

wCE

Returns, w

RP

Definitions:

wCE U1(E{U(w)})

RP E{w} wCE

U(E{w})

U(w2)

U(w1)

E{w} w2w1

Optimization

• Poorly-behaved response surfaces

• Computationally-intensive

• Robust methods:

– Simulated annealing

– Genetic algorithms

• Compromise: grid search

Crop Planted after this Date

Profit

($/ha)

Nitrogen

Applied (kg/ha)

optimal region

Value for maize management, Kenya• Decisions that are

optimal on average are usually far from optimal.

• Skillful forecasts can inform management that is closer to optimal for given weather conditions.

1995 (dry)

1994 (wet)

average weather

yieldincomeoptimal N

d

c

f

e

b

a

CLIMATE RISK- SEMI ARID VILLAGE AT MEHABUBNAGAR

• CROP (Specific) - RAINFED MAIZE, RAINFED Bt.COTTON

• LIVESTOCK (Specific) - SHEEP

• RISK MGT AT – FAMILY LEVEL – LIVELIHOOD PERSPECTIVE– COMMUNITY LEVEL– GOVERNMENT AND BANK

• VARIABILITY OF RISK – AT FARM SCALE – IN TIME AND SPACE

• LIVELIHOOD OPTIONS– COMBINATION OF ENTERPRISES

Analyzing climate risks and risk management approaches

at community/village levelSheshagiri Rao

CROP YIELD SCENERIO at study village

CROP NORMAL YIELD kg/ha

GOOD YEILD kg/ha

YEARS POOR YEILD kg/ha

YEARS BEST YEILD

kg/ha

Maize Red 3,750

Black-5,000

Red 5,000

Black-7,500

2000,1998, 2007

Red 1,250

Black-2,500

2001,2002, 2003, 1997

15,000 (Black)

Bt. Cotton

1750 2500 2007 500 2008 3000

Decision on Climate & other information (minimum)

Lead time

Additional cost

Additional benefit (correct

choice)

Penalty (Wrong

choice)

Choice of crop- maize or cotton?

Seasonal total. Long dry spell timing

Before sowing

None

30-60% higher yield

Lose benefit

Best sowing window in – June 1 wk to Aug 1 wk

Distribution of wet/dry spells, Crop simulation runs

Before sowing

None

20-80 % higher yield

Lose benefit / can not sow the crop

Moisture stress management for the crop

Dry spell at silk formation stage (60-70 das)

7-10 days ahead

Irrigation

30-60% higher yield

Cost

Aphids management

Wet spells in Vegetative growth, silk formation

7-10 days ahead

Plant protection

10-30% higher yield

Cost


Lead time

Additional cost

Additional benefit (correct

choice)

Penalty (Wrong

choice)

Timing and number of top dressing

Wet spells in Vegetative growth, silk formation

2-3 weeks ahead

None 10-30% higher yield

Inefficient use, loss of benefit

Management of water logging, Downy mildew and wilt in Black soils

Long wet spells in Vegetative growth, silk formation

4-7 days ahead

Drainage, Plant protection,

20-40% higher yield

Cost / Loss of benefit

Nutrient management in Low temp.

Low Minimum temp. during veg. silk formation

4-7 days ahead

Zn foliar spray

10 % higher yield

Cost/ loss of benefit


Lead time

Additional cost

Additional benefit (for correct

choice)

Penalty (for Wrong

choice)

Best sowing window in – May 4th wk to June 4th wk

Distribution of wet/dry spells, Crop simulation runs

Before sowing

None 20-80 % higher yield

Lose benefit / can not sow the crop

Moisture stress management for the crop

Dry spell at boll formation stage (90-120 das)

7-10 days ahead

Irrigation 30-60% higher yield

Cost / loss of benefit

Thrips management- vector for Leaf curl/ cotton necrosis

Dry spells at Veg. & boll form stage

7-10 days ahead

Plant protection

10-40% higher yield


Mealy bug and Mirid bug (affects bolls) management

Not clear 7-10 days ahead

Plant protection

10-40% higher yield


Cotton


Lead time

Additional cost


choice)

Penalty (for Wrong

choice)

Aphids management

Wet spells in Vegetative growth, boll formation

7-10 days ahead

Plant protection

10-40% higher yield

Cost/ Loose benefit

Timing and number of top dressing

Wet spells in Vegetative growth, boll formation

2-3 weeks ahead

None 10-30% higher yield

Inefficient use, loss of benefit

Management of water logging, and wilt in Black soils

Long wet spells in Vegetative growth, boll formation

4-7 days ahead

Drainage Plant protection,

20-40% higher yield

Cost / Loose benefit

Nutrient management in Low temp.

Low Minimum temp. during veg. boll formation

4-7 days ahead

Zn, B, Mg foliar spray

10 % higher yield

Cost/ loss of benefit

Cotton


Lead time

Additional cost


choice)

Penalty (for Wrong

choice)

Mango Hopper management during flowering

Temperature in January and February

7-10 days

Cost of sprays

10-40% Cost/ loss of benefit

Anthracnose Management at new flush

Humidity 7-10 days

Cost of sprays


Manage increase Vegetative growth at flowering

Wet spells in October November

10-20 days

Spray of flowering promoters


Effect of hail storms

Timing of hail storms

3-7 days

None- (Early harvest?)

10-40% Loss by early harvest

Manage impact of High temperature

Timing and intensity of the event

3-7 days

(Irrigation ?)

10-80% Cost / loss of benefit

Mango


Lead time

Additional cost


choice)

Penalty (for Wrong

choice)

Scheduling and number of de- wormings (2 parasite species )

Humidity, Wet spells. Fecal egg count (Sept- Oct) ??, Faciola Snail as vector

10-30 days

Reduce costs by 2-6 times

30 -200 Rs per animal

Loss of benefit/ Morbidity of animal

Manage ET (Enterotoxaemia), fodder management

Timing and intensity of first drenching

7-15 days

None (Preventive? )

Reduce 5-40% loss


Manage Blue tongue- Diptera Vector population dynamics

Rainfall, Temperature in November, December

7-15 days

None (Preventive? )

Govt. preparedness

Reduce 5-70% loss of lambs


Sheep – One of the highest district level Population in the nation

(AP has the highest amongst states)


Lead time

Additional cost


choice)

Penalty (for Wrong

choice)

Fodder growth in spatial spread of their migration route

Total rainfall, Late season rains

2-3 weeks

None 20-300 Rs per animal

Loss of benefit/ additional loss

Availability of Weeds as fodder in crop lands

Late season intense wet spells

2-3 weeks

None 20-300 Rs per animal

Loss of benefit/ additional loss

Sheep

CROP -TOTAL CLIMATE RISK COMPONENT

• From end to end- Land preparation, crop sowing – TO

Harvest and post harvest operations• Consider both

– Direct impact- by moisture stress, water logging and on Crop physiology

– Indirect impact – by triggering rapid increase of pests, diseases and vector populations that are already endemic.

• In any particular year a particular combination of such ‘adverse events’ would occur

• It is possible to construct simple models for such climate impact by using – Existing literature– Expert knowledge of farmers, field researchers

NOTE

• All further slides refer to Rainfed groundnut at Anantpur

• These are illustrative of methodology

• similar questions (to the ones mentioned here) were asked by farmers in the study village.

Plot level = Profit / loss is rain+ many others05 =43 cm, 06=32cm, 07=52cm, 08=57cm

P rofit/Ac _G roundnut_ (All Villag es -2005-06 to 2008-09)

-8000.0

-6000.0

-4000.0

-2000.0

0.0

2000.0

4000.0

6000.0

8000.0

10000.0

12000.0

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

106

111

116

121

126

131

136

141

2005 2006 2007 2008

At plot level- Yield variation and rain- relationship is much weaker than EXPECTED

05 =43 cm, 06=32cm, 07=52cm, 08=57cm

G roundnut Y ield/ Ac (All Villag es 2005-06 to 2008-09)

0.0

5.0

10.0

15.0

20.0

25.0

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

106

111

116

121

126

131

136

141

146

2005 2006 2007 2008

Cost of Cultivation

Anantpur District average groundnut yield- (1975-1995) - Avg rain-47 cm

TOTAL CLIMATE RISK FOR GROUNDNUT CROP

Simple model for Rainfed Groundnut At Anantpur- an example

Climate – Direct impact

Climate- indirect impact

Validation of Model Prediction and Field data

6 villages in Anantpur region

• Located in 3 separate Mandals, distributed in an area of about 4000 sq km

• Data from Marginal and small farmers, Vulnerable sections to climate risk

• Sample of 20-40% of the total families in the community

• Family wise data collection from 2005 to 2008

Community level Livelihood options at 3 villages of Anantpur

Families grouped on similarity of income sources for livelihood (USER GROUPS)

Fruit orchard + irrigated farm +Govt. Contracts

4% Rainfed farm + wage labor39%

Rainfed farm + irrigated + sheep/goat keeping

30%

Sheep/ goat keeping + rainfed + wages

13%

Wage labor + sheep/ goat/ pig keeping

14%

Top priority information needs of families (weighted w ith Number of families)

Marketing of Farm Produce

5%

Electricity Supply and usage

10%

Credit7%

Peanut, PP Yield, cropping systems

15%

Opportunities of Wages13%

Rainfall prediction - total, distribution

20%

Mgt. of Diseases, pests, Parasites of crop, tree,

sheep17%

Grants and subsidies from Govt.

13%

Family wise Annual income distribution- 6 villages

Total Annual Income from work 1 and 2

0

20000

40000

60000

80000

100000

120000

Village & Code number of family

Annua

l Incom

e

Family wise Cattle population in 6 villages.

Total Number of Animals in all 6 villages

0

5

10

15

20

25

C1C1

8 C9C1

1C1

0 C4 P14 P7

P17

P12 V2 V8 V6 V3 G5

G11

G19 G1

G22 R6

R25 R5

R21

R14

R16

S23

S11

S14 S7

Code number of families

Numb

er of

anim

als

Family wise sheep and Goat income- 6 villages

Total Income from Sheeps & Goats

05000

1000015000200002500030000350004000045000

Code number of families

Total Inc

ome fro

m shee

ps & g

oats

How much credit?

Freequency distribution of credit

55

30 30

14

0

10

20

30

40

50

60

0 to 20000 21000 to 40000 41000 to 60000 61000 to 80000

Class interval

num

ber o

f peo

ple

Reasons for first debt ? – crop (during bad year), bore wells, sheep are the big reasons

Number of people debit taken (First time)

11 11 22 222 33 4

0 011 11 111 12 3

7

001 11 11 123 34

00 112

23

0 023 4

710

0 011 1144

12

0

5

10

15

20

25

For h

and

For m

arria

geP

urch

asin

gP

urch

asin

gB

uilt

for

Cap

ital

For C

rops Fo

rFr

om p

ast

2005

- for Fo

r20

02 B

ore

Bul

it Fo

rC

rops

,Mar

riaE

duca

tion,

Hea

lthI t

ook

15P

urch

ased

Pur

chas

ing

Yea

rly,

for B

ore

For c

apita

ls19

98 fo

r

1995

2000

land

2002

trad

ing

2004

-3

mon

ths

6 ye

as19

90 b

ullu

ck 1990

2002

- cr

op19

95 h

ouse 2003

In J

une

2006

In 2

004

for

1995

Cro

p

Bui

ldin

gC

apita

l for

For C

apita

l20

04 fo

rFo

r Cro

ps

1990

- H

ome

2002

-20

04 -

Milc

h20

05 -

2002

-S

heep

s19

91

Reasons

Num

ber o

f peo

ple

Debt trap (3rd Default) - Reasons

Reasons for debt (3rd Time) & number of people

1 1 1 1 1 1 1 1 1 1 1

3 3

5 5

10

0

2

4

6

8

10

12

Reasons

Nu

mb

er o

f p

eop

le

Govt. programs as a safety net

NO of benificiaries of different govt programmes (villagewise)

1 13

5 6

15

0 0 1 1 0 0 1 1 1 1 2 26 7 8 9

0 0 1 1 1

20

0 0 13 3

5 6

0 02 2 2 2 2

0

5

10

15

20

25

Gram

eena

Child

Gram

eena

Kruth

aTh

rough

gove

rnmen

tE.

G.S

Patha

kam

Unde

r M.L.

A.Qu

otaLo

an ta

ken

unde

r velu

gu

Bask

et so

ldMu

labari

Shed

Metta

sagir

iSG

SYVe

tti sh

akiri

Shee

p loa

nBu

ndco

nstru

ction

Crop

laon hous

eWa

ter sh

ed,

Home

land

,Me

ttikatt

aluHo

use l

and

pakk

a Illu

Varal

aksh

mi Durga

Send

riya

Jala

Jeev

an

Assia

nmen

tLa

ndSiv

a Jam

a Siv

a Jas

thiAs

signe

dPa

tta - 5 Ho

use

Hous

eSc

heme

-SC

Coorp

eratio

nWa

ter sh

edpro

gramm

eSt

one

Cleara

nce

Rs. 1

0,000

Loan

throu

gh

Govt programmes

NO of

benif

iciari

es of

dif

feren

t gov

t pro

gram

mes

CPRs as the safety netNumber of families depends on common land for fodder

1 1

4

9

19

0

5

10

15

20

Chinnapalli Venkatampalli Rachur PeddaKondapuram

Shyapuram

Village

Num

ber o

f fam

ilies

Number of families depends on common land for fodder

Highest number of animals not with the largest of farms

Total Land Vs Total Number of Animals

05

10152025303540

Code Number of the Family

Tota

l lan

d &

Tota

l Num

ber

of a

nim

als

Total Land

Total Number of Animals

Mid size farms have the largest credit

Total Land & Total credit

0

5

10

15

20

25

30

35

40

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101106111116121126131136141146151

Code number of the family

Tota

l Lan

d

0

50000

100000

150000

200000

250000

tota

l Cre

dit

Total Land

Total Credit

Using climate information to manage crop mixes

Dr. Reddy

Timely onset Late onset

Black soil Red soil Black soil Red soil

Cotton Maize** Maize** Castor

Maize* Redgram Sunflower

Sunflower

Green gram

Castor

Onset of sowing rains and crop choice

* Long duration maize** Short duration maize

Agronomic options during dry spell

Red soils Maize Reduced dose top dressing of N application Supplemental irrigation (Tolerates 8-10 days dtress) Protect crop from sucking pests

Black soil Cotton Reduced dose of top dressing of N application Supplemental irrigation Maize Supplemental irrigation (Tolerates 15 -20 days

Under severe drought conditions maize can be removed and chickpea can be taken-up of in black soil as rabi crop

Distribution of sunflower seed in Kurnool district during month of July 2002 in A.P

Implications of rain forecasts

Policy decision

Simulated Potential Maize Yields Andhra Pradesh

0

100

200

300

400

500

600

700

800

900

1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Gra

in y

ield

(g

/m2

)

Bairanpally Srirangapur

Maize: “Rainfall distribution from 55 days after sowing to maturity is important”

y = -0.0018x2 + 2.774x - 353.28R2 = 0.3856

0

100

200

300

400

500

600

700

800

900

0 200 400 600 800 1000 1200

ESW_sow+in-crop rainfall (mm)

Gra

in y

ield

(g

/m2)

Bairanpally Poly. (Bairanpally)

Higher yielding crops with rainfall of < 600 escaped swdef < 0.75, support for local agronomist’s rainfall distribution explanation

Water stress (post flag leaf emergence) explained most but not all of the low

yieldsLow yield (<600 g/m2)

Average water stress (swdef_photo)

Year Grain yield

(g/m2)

Germ-Emerg

Emerg-EndJuv

EndJuv-FlorInit

FlorInit-FlagLf

FlagLf-Flower

Flower-StartGF

StartGF-EndGF

1971 7.3 1 1 1 0.855 0.453 0.340 0.795 1972 149.2 1 1 1 0.893 0.597 0.561 0.373 1965 349.0 1 1 1 0.920 0.538 0.766 0.847 1994 353.8 1 1 1 0.897 1 1 1 1982 354.2 1 1 1 0.977 0.694 0.741 0.658 1985 374.4 1 1 1 1 0.997 0.983 0.506 1968 388.6 1 1 1 1 0.721 0.629 1 1984 458.3 1 1 1 0.985 0.564 0.917 0.628 2001 507.4 1 1 1 0.977 0.752 1 1 2007 562.8 1 1 1 1 1 0.909 1

Maize crop calendar

29

(16-22Jul)

28

(9-15Jul)

27

(2-8Jul)

26

(25-01Jul)

25

(18-24June)

FebJanDecNovOctSepAugJulJunSowing week

Est CD TA+SL G.fil Mat.





0.00

0.20

0.40

0.60

0.80

1.00

1.20

S td . w e e k

Pro

ba

bil

ity

2nd week 3rd week

Porbability of occurrence two and three consecutive dry spells

Cotton crop calendar

29(16-22Jul)

28(9-15Jul)

27(2-8Jul)

26(25-01Jul)

25(18-24June)

FebJanDecNovOctSepAugJulJunSowing week

Est CD Fl&BD Maturity





0.00

0.20

0.40

0.60

0.80

1.00

1.20

19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

Std. week

Pro

bab

ility

2nd week 3rd week

Porbability of occurrence two and three consecutive dry spells

Dynamism of Agricultural Risk Drs. D. R. Reddy, Amor Ines, Sheshagiri Rao.

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Transcript of Dynamism of Agricultural Risk Drs. D. R. Reddy, Amor Ines, Sheshagiri Rao.