Climate Applications and Agriculture: CGIAR Efforts, Capacities and Partner Opportunities
Climate Applications and Agriculture: CGIAR Efforts, Capacities and Partner Opportunities.
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Transcript of Climate Applications and Agriculture: CGIAR Efforts, Capacities and Partner Opportunities.
Statistical downscaling of GCM rainfall prediction – observed rainfall in two regions
JASO-Nandyala (1970-03)
0
200
400
600
800
1000
1200
1968 1973 1978 1983 1988 1993 1998 2003
Year (0.66)
Rai
nfa
ll (m
m)
observed
predicted
96
143130127
145
12010196
136
192
115
185
149164
5645
114
48
132
59
36
2
Jun Jul Aug Sep Oct Nov
Months
Rainfall prediction of ECHAM-4 downscaled to monthly, and observed rainfall for Nandyala during 2003
Monthly_prediction Actual-03-ndk Actual-03-ndl Actual-03-vor
JJASO-ATP (1970-2003)
0
100
200
300
400
500
600
700
800
900
1968 1973 1978 1983 1988 1993 1998 2003
Year (r=0.4675)
Rai
nfa
ll (m
m)
observed
predicted
58
40
7
54
73
65
85
48
129
22
71
96
109
150
54
00900
J un J ul Aug Sep Oct Nov Dec
Months
Rainfall prediction of ECHAM-4 downscaled to monthly, and observed rainfall for Anantapur during 2003
Monthly_prediction Observed-2003-SGML Observed-2003-ATMK
Results of farmers’ participatory cropping decisions based on climate prediction
Anantapur region Crop management decisions were based on climate,
and revolved around peanut sole or intercrop systems
Rainfall prediction failed in JAS months with low rainfall
Kurnool region Crop management decisions based on climate
prediction by 1/3 of the farmers
Rest based decisions on crop rotation and commodity market prices
Farmers achieved higher productivity with intercrop systems (>50%) than either sequential double cropping or post rainy season sole crop, due to terminal stress.
Potential benefits from forecast based farming in Kenya
Type of seasonFarmer practice
Forecast based farming with 35,000 plants ha-1 and
30 kg N ha-1 40 kg N ha-1 60 kg N ha-1
Dry 555 951 (71) 1052 (90) 1206 (117)
Normal to wet 666 1879 (182) 2286 (243) 2822 (323)
All 613 1467 (139) 1747 (185) 2151 (251)
Gap in potential and achievable yields with forecast based farming in normal to above
normal seasons – Katumani, Kenya
Predicting Global Warming Effects
Global maize production could fall 10%, especially harming developing countries and the poor, according to CIAT and ILRI scientists
Period 1 Decisions Period 2 Decisions Period 3 DecisionsPre-planting Planting Weeding and intercropping
Fertilizer-Phos Plant Millet – Early or Late Fertilize-NitrogenBuy/Sell Livestock Fertilizer-Phosphorus/Nitrogen Transplant ricePlant rice nursery Buy/Sell Livestock Plant Cowpea-DensityTransplant rice Wage Labor – Buy or Sell
Weed millet/rice
Effects of Various Technologies and a Subsidy on Adoption of Effects of Various Technologies and a Subsidy on Adoption of Fertilizer on a Representative Farm in the Sahelo-Sudanian Fertilizer on a Representative Farm in the Sahelo-Sudanian
Zone in Niger Zone in Niger
Policy or Fertlizer use Rainfed crop % changeprogram (ha) income (US$) crop income
1. Current practices N/A 486 -
2. Improved Short-cycle 0 631 20 cultivars
3. Phosphorus only 2.1 685 41
4. Long-cycle cultivars* 1.5 651 34
5. Input subsidy (10%) 1.2 657 35
* Combine with both N and P fertilizers. Exchange rate: 273 FCFA/US$ (IMF, 1990). Source: adapted from Sanders et al. (1996).
Figure 1: Development Paths of Agricultural Systems in Semi-Arid Areas
A. SubsistentPastoralism and
Agropastoralism (low input)
B. Semi-subsistentExtensive Integrated
(low external inputs)
C. Semi-commercial Intensive Integrated
(high external inputs)
D. Commercial IntensiveSpecialized
(high external inputs
Population Pressure
Acc
ess
to M
arke
ts
Rainfall limiting to intensification
Rainfall conducive to intensification
E. CommercialExtensive Specialized
(low input)cow-calf operations
Rainfall
Climate: what is different about West Africa?
There are no such things as climate ‘normals’ in sudano-sahelian West Africa “What is ‘normal’ to the Sahel is not some […] rainfall total […] but variability of the rainfall
supply in space and from year-to-year and from decade-to-decade” (Hulme, 2001)
Climate: what is different about West Africa?
High variability in both cases but…
(reproduced from IPCC, 2001)
Sahel: higher variations on decadal time
steps (low frequency)
SEA: higher variations on yearly time steps (high frequency)
does this mean relatively more risk for an annual crop /
farmer in SEA?
not necessarilybecause :
Predictability is higher in SEA (both yearly and in the long term)
Risk = uncertainty x vulnerability
New Tools to Assess and Monitor Drought and
Desertification
Southern Africa, March 2002Drought Index (%)
Difference with average 1999-2001 (%)
Farmer Perceptions of Drought
What matters to farmers: how drought affects their food security and livelihoods
A DDPA-Sponsored study in Burkina Faso by the Univ. of Wageningen
Conclusion: help farmers make better use of limited rainfall
Village ICT Hub at Addakal, South India
• Located in a highly drought-prone area; covers 37 hamlets, 45 000 population (app)
• All-women micro-credit federation owns the hub premises; 4500 members
• Internet connectivity available; small group of women trained in IT and info-mediation on agri/drought matters
• PRA for info needs conducted and updated; regular feedback received
• Now acts as informal extension access point
New program on Drought Preparedness in
Maharashtra
NASHIK
PUNE
AHMEDNAGAR
30,000 rural youth receiving a 4-hr module on drought literacy for monitoring activities
Content from VASAT adopted by Maharashtra Knowledge Corporation Ltd. And Pune Univ.
VASAT (Africa)
ww w.vusat .org
(Interface of community radio and Internet through WorldSpace technology)
VASATVirtual Academy
for the Semi-Arid Tropics(Reaching the Un-reached)
A community-based distance learning coalition for SSA WITH THE DMP
Desert Margins Program
Community Radio Hub in Kahe, Niger
• Uses WorldSpace digital satellite radio technology to receive info from the Web
• Hosts community radio station covering 50 sq km area
• Functional since September 2004
CGIAR’s assets to institutionalize and further operationalize climate applications
• Major repository of dynamic knowledge on GxE (genotype x environment) interactions can be activated to target farmer-friendly biotech interventions for improved management of climate variability and change (CIMMYT, CIP, ICRISAT, IITA, IRRI…)
• Existing poverty mapping expertise can be expanded to address climate risk management following the [risk = uncertainty x vulnerability] paradigm, e.g. to determine priority focus regions for applications of climate forecasting (CIAT, IFPRI, ILRI…)
• Strong capacity building and ICT/KM capacity can be mobilized to help solve communication bottlenecks linked to user understanding of the abstract, probabilistic nature of forecasts (VASAT, …)
• Combination of highly decentralized, network structure and international mandate can help tailor options for local climate management while ensuring standardized, science-based methodologies that allow for regional and global assessments of climate management impacts
Future CG Contributions
• Combining indigenous and science-generated knowledge• Advancing knowledge on GxE [genotype x environment]
interactions• Building climate science & monitoring capacity• Using ICT4D to communicate climate information to
farmers• Combining bio-economic modeling and advanced
computing power to improve use and impact of adaptive recommendations
• Combining poverty and climate variability mapping
[risk = uncertainty x vulnerability] • CG very good at networking