who owns groundwater? climate information contributes to better water management
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Who owns groundwater? Climate Information contributes to better water management. Ramasamy Selvaraju. Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, INDIA. Smallholder farming systems. - PowerPoint PPT PresentationTRANSCRIPT
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Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, INDIA
Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, INDIA
Who owns groundwater?
Climate Information contributes to better water management
Who owns groundwater?
Climate Information contributes to better water management
Ramasamy Selvaraju
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Smallholder farming systemsSmallholder farming systems
1. Smallholder farms have undergone substantial changes in the last century increasing their exposure to climate variability
2. 78% of total operational holdings occupies 32% of total agricultural area
3. The number of shallow tube wells and deep tube wells has increased by about 100% over the last 10 years
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Lorenz curves and Gini coefficients for the distributions of total farm income
(FI) and agricultural income (AI) under various irrigated
cropping systems
Lorenz curves and Gini coefficients for the distributions of total farm income
(FI) and agricultural income (AI) under various irrigated
cropping systems (d) Overall
0
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0 0.2 0.4 0.6 0.8 1
FI (G = 0.42)AI (G = 0.45)Absolute equality
(a) drylands
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0 0.2 0.4 0.6 0.8 1
FI (G = 0.0.39)AI (G = 0.32)absolute equality
(b) Irrigated uplands
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0 0.2 0.4 0.6 0.8 1
FI (G = 0.0.37)AI (G = 0.37)absolute equality
(c) Irrigated lowlands
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0 0.2 0.4 0.6 0.8 1
FI (G = 0.34)AI (G = 0.36)absolute equality
Cumulative fraction of farmers
Cum
ulat
ive
frac
tion
of in
com
e
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What needs to be done?What needs to be done?
Climate is one of the many factors influencing agriculture
Make the farmers to understand the climatic risks / opportunities
Help to manage the system through local knowledge and scientific tools
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To utilize the ability to predict climate variability and change on range of scales to improve decision making using climatic risk management strategies in agriculture at farm, regional and national scales for enhancing resilience and sustainability.
To utilize the ability to predict climate variability and change on range of scales to improve decision making using climatic risk management strategies in agriculture at farm, regional and national scales for enhancing resilience and sustainability.
AIM
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Overall aim of the projectOverall aim of the project
To assess and manage the impact of climate variability on the irrigated crop production systems to improve smallholder food security in a highly vulnerable semi-arid India.
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Specific objectivesSpecific objectives To document the information on climate and its predictability,
water resources and water need of the irrigated crop production systems.
To assess the impact of El-Niño Southern Oscillation (ENSO) on water availability and on crop yield through system simulation approaches.
To develop a ENSO based resource allocation and cropping decision framework for the smallholder situations.
To demonstrate the benefit of seasonal climate forecasting to the smallholding farmers, extension and PWD workers of irrigated cropping systems to manage climatic risks.
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Diverse Cropping SystemsDiverse Cropping Systems
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Correlation between monthly SOI values and seasonal rainfall
Correlation between monthly SOI values and seasonal rainfall
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Cor
rela
tion
Coe
ffic
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SMWM
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The spatial pattern of correlation coefficient between JJA SST anomalies and (a) summer and (b) winter monsoon rainfall
The spatial pattern of correlation coefficient between JJA SST anomalies and (a) summer and (b) winter monsoon rainfall
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0
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ne
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ep
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197019751980198519901995Year
ObservedHindcast
R = 0.512
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d g
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ut yie
ld
, kg
/h
a197019751980198519901995
Year
ObservedHindcastR = 0.554
Observed and hindcast station rainfall and simulated groundnut yields based on transformed, cross-validated
ECHAM predictions.
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Spatial variation in ground water table (depth from surface in meters)
Spatial variation in ground water table (depth from surface in meters)
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The average monthly rainfall and potential evapotranspiration (PET) under ENSO phases
The average monthly rainfall and potential evapotranspiration (PET) under ENSO phases
(b) PET (mm)
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w arm
cold
neutral
(a) Rainfall (mm)
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w arm
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Decadal water requirement of irrigated maize (120 days duration) under various ENSO phases
Decadal water requirement of irrigated maize (120 days duration) under various ENSO phases
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Decades (10 days)
Irri
ga
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nt (
mm
)
warm
cold
neutral
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Water balance components for irrigated maize (June-September) under ENSO composites
Water balance components for irrigated maize (June-September) under ENSO composites
Particulars Warm Cold Neutral
Effective rainfall (mm) 161 196 198
ETcrop (mm) 640 562 615
Total gross irrigation requirement (mm)
600 450 500
Irrigation efficiency (%) 77 83 82
Actual water use by crop (mm) 640 562 615
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Irrigation water requirement (mm) of crops conditioned on ENSO phases
Irrigation water requirement (mm) of crops conditioned on ENSO phases
Particulars Warm Cold Neutral Banana 1411 1206 1346Vegetable – I 511 411 468Summer Maize 600 450 500Vegetable – II 521 451 486Winter Maize 529 455 525Total 3572 2973 3325
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Monthly irrigation requirement of crops (banana, vegetable-1, summer maize, vegetable -2 and winter maize)
Monthly irrigation requirement of crops (banana, vegetable-1, summer maize, vegetable -2 and winter maize)
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Banana
veg-1
S-Maize
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m3 )
(a) Warm
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Farm level water availability scenario under various ENSO phases
Farm level water availability scenario under various ENSO phases
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3) warm
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Area under irrigation for various crops conditioned by ENSO phases and price scenarios
Area under irrigation for various crops conditioned by ENSO phases and price scenarios
(a) High price
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Banana S-veg S-Maize w-veg w-maize
Are
a (
ha
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warmcoldneutral
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Banana S-veg S-Maize w-veg w-maize
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Banana S-veg S-Maize w-veg w-maize
(b) Medium price
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Banana S-veg S-Maize w-veg w-maize
Are
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warmcoldneutral
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Gross margin (Rs.) from irrigated area of a 3 ha farm under ENSO phases and produce price scenarios
Gross margin (Rs.) from irrigated area of a 3 ha farm under ENSO phases and produce price scenarios
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low medium high
Price Scenario
Gro
ss M
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in (
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warmcoldneutral
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Economic value of climate forecasts in dryland systems
Economic value of climate forecasts in dryland systems
Crop Decision Economic value (Rs./ha/year)Neg Pos Fal Ris Neu
Groundnut Stand density 2546 0 2049 0 0
N Fertiliser 17 79 115 0 0
Crop Choice 4128 0 4059 0 0
Cotton Sowing window 0 751 78 0 580N fertiliser 600 290 548 0 0
Sorghum(Rabi)
Stand density 43 0 272 83 0
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Are simulation models useful for smallholder farmers?
• Management responses to seasonal climate forecasts (developing the options)
• Converting climate forecasts into management options to satisfy the diverse requirements of smallholder farmers
SOI Phase
Negative HD + FFS
Negative LD + TFS
Falling HD + FFS
Falling LD + TFS
Gro
ss M
arg
in (
Rs./
ha)
10000
8000
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0
-2000
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-6000
(a) Avinashi
• Impacts conditioned on forecasts
SOI Phase
Negative Positive Falling Rising Near zero All years
Gro
ss
Ma
rgin
(R
s./h
a)
16000
14000
12000
10000
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0
-2000
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-6000
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Media for climate informationMedia for climate information
Climate workshops – “Farmer groups”
We learned to
• Skip the mass media (message distortion)
• Use generic methods with slight modifications for different target groups
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Can farmers understand probabilistic climate forecasts?
Can farmers understand probabilistic climate forecasts?
Yes, but …
Tried to convert the probabilities in to deterministic forecasts (320 x 0.65 = 208 mm)
Tried to convert into subjective and convenient categoriesGood rainfall / Low rainfall
Seems to understand the probabilistic forecasts, but ignores the probability and remembers only the rainfall quantity
Frequent contacts and gambling analogies
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• Awareness is the first step for successful implementation of climate and agriculture programmes
• Decision capacity of the farmers can be improved through climate education programmes at different levels
Improving the knowledge and decision capacity
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ChallengesChallenges
Message distortion and artificial skill
Farmer ‘feel’ that a forecast is “right” in neighboring village although there is no “right” or “wrong” in probabilistic forecasting
We need to understand that climate greatly affect the performance of technology
The information provider needs to understand possible consequences of options
Wrong interpretation may lead to conflicts
Many pseudo forecasters and forecasts without understanding of the physical mechanisms are emerging
Spatial variability in rainfall needs to be better addressed
Climate knowledge is more than just providing a forecast
How can we combine and evaluate the local indigenous knowledge with the scientific technologies?
Climate communication – Capacity building at various levels
Ownership of the climate information, options and decisions by the end users
Improving the predictability
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Participatory farmer interactionsParticipatory farmer interactionsWe engaged with local farmer groups to
understand their agricultural system and their needs
We considered their practices and rules of thumb and considered those as part of our system analysis framework
We developed options and discussed risks and opportunities as well as consequences of management alternatives through simulation modelling
We encouraged farmers to make informed decision after understanding the risk and consequences
We solicited feed back and responses from farmers and reconsidered options
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How demand-driven research helps for participatory co-learnig?
How demand-driven research helps for participatory co-learnig?
Focus group meetings
On-farm varietal evaluation
On-farm experiments on varietal response to
drought
New insights into system analysis
Analysing the options
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AWARENESS ON WATER RESOURCE MANAGEMENTAWARENESS ON WATER RESOURCE MANAGEMENT
Public Works Department Water Technology Centre Geology Department Ground Water Board Local Political presidents State Agricultural Extension Farmers organizations NGOs
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Knowledge to ScienceKnowledge to Science
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CAPACITY BUILDING ACTIVITIES CAPACITY BUILDING ACTIVITIES
National level training workshop on “Systems Approach for Climatic Risk
management in Agriculture”
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Training CurriculumTraining Curriculum system analysis and modeling assessment of impacts of climate variability and change
on agricultural systems climate forecasting methods use of climate forecasts in farm decision making linking climate and bio-physical models to explore
management options and outcomes management of risks in agriculture associated with
drought, floods and cyclones. application of remote sensing in climate risk
management
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PublicationsPublications
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AcknowledgementsAcknowledgements