Climate and Agricultural Risk Drs. Reddy, Amor Ines, Sheshagiri Rao

Overview I. Drivers of agriculture risk (climate and non-climate) II. Analyzing variability at different spatial and temporal scales –Yield variability and spatial scales –Rainfall variability across time III. Analyzing roles of climate and non-climate factors in yield variability –Using de-trending to separate low-frequency and high frequency influences on crop yield variability –Yield Analysis: Mahabubnagar case

… Overview contd… IV. Implications of variability for decision making –Decisions are dynamic –Limitations of using average values V. Identifying various levels of spatial analysis –Options for decision making on climate risk and opportunities –Time horizons in decision making –Role of different decision makers VI. “Good” and “bad” years –What are good and bad years? –Methods for analyses: Z-score approach and Percentile Threshold approach VII. Weather Manager: Tool for analyzing weather data

I. Drivers of Agricultural Risk and Across Scales Climate (temperature/rainfall extremes) Prices (of seeds/inputs, mandi prices) Institutions (banks and access to credit, community support groups, etc) Policies (subsidies, government relief programs, water/land access rights, etc)

II. Analyzing Variability Across Scales Yield Variability and Spatial Scales Rainfall Variability across Time

Scale and Yield Variability Variability of groundnut yields at multiple scales, residuals about smoothed trend.

Scale and Yield Variability Variability of groundnut yields at multiple scales, residuals about smoothed trend.

Scale and Yield Variability

Variability of groundnut yields at multiple scales, residuals about smoothed trend. Scale and Yield Variability

Variability of groundnut yields at multiple scales, residuals about smoothed trend. Scale and Yield Variability

Spatial (Rainfall) Variability Dependable rainfall (mm) in different regions of Andhra Pradesh

Temporal (Rainfall) Variability Annual rainfall (mm) trend in Andhra Pradesh Trend line Mean Rainfall

Temporal (Rainfall) Variability Rainfall deviation (%) over Andhra Pradesh

III. Climate variability (and de-trending) Analyzing roles of climate and non-climate factors in yield variability –Using de-trending to separate low-frequency and high frequency influences on crop yield variability –Yield Analysis: Mahabubnagar case

Impact of the deficits of the monsoon rainfall significant despite the technology inputs Climate variability and de-trending

Yield Analysis – Mahabubnagar Example III. Climate variability and de-trending

Yield reconstruction using three datasets Kg/ha Year

Yield reconstruction and de-trending Kg/ha Year

Yield residuals (=Yobs/Ytrend-1) Kg/ha Year

Yield reconstruction and de-trending A low-pass Fourier-based smoother is used Kg/ha Year

Residuals Yield residuals (=Yobs/Ytrend-1)

Residuals Yield residuals (=Yobs/Ytrend-1)

IV. Implications of Variability for Decision Making Decisions are dynamic Limitations of using Average Values

Station Rainfall Variability Months

Average Monthly Rainfall

Seasonal Rainfall Variability (JAS)

JJA JAS Rainfall amount, mm Exeedence Probability of Rainfall

V. Levels of Spatial Analysis Spatial levels decision making Options for decision making on climate risk and opportunities Time horizons in decision making Role of different decision makers

FOREFITED OPPORTUNITY CRISIS HARDSHIP Managing the Full Range of Variability

Spatial levelDecision byCRM OPTIONSOPPORTUNITIES (good events) PlotFamilyChoice of variety, fertilizer dosage, irrigation Increase in cropping intensity. Family / farm FamilyCrop, enterprise choiceMix of enterprises, livestock, trees CommunityFamilies/ local institutions Use of CPRs, watershedsImprovements in CPRs Region (Sub district) Govt. banks, and other institutions Subsidies, crop insurance, Govt. Schemes Improvements in CPRs DistrictGovt. banks, and other institutions Subsidies, dev. Schemes, Local self Govt. Policy- Watersheds, improved farming State/Nation al Govt. banks, and other institutions, Policy Loan waiver, crop insurance, credit policy, REGS, Drought relief measures Policy- efficient irrigation, prices, nutrient use, farming methods, trade Levels of Spatial Analysis

Diversification and Risk Low Correlation + Diversification = Reduced Risk A & B independent random normal C t = 0.5 A t B t SD A = 1.03, SD B = 0.96, SD C = 0.51

Diversification and Risk More can be better!

Avinashi, TN Optimal crop mix: –groundnut- sorghum –cotton Maximize CE income Obj. fxn.CottonG’ndnutmeanSD risk-neutral100%0% mod. risk averse32%68% Diversification and Risk

Crop mixes with Negative correlation in yield – Non overlapping critical periods Levels of Spatial Analysis

Family wise Cattle population in 6 villages. Levels of Spatial Analysis

Family wise sheep and Goat income- 6 villages Levels of Spatial Analysis

Common Property Resources, Safety Net

Highest number of animals not with the largest of farms Levels of Spatial Analysis

VI. “Good” and “Bad” years What are good and bad years? Two methods for analysis – Percentile Threshold Approach – Z-score Approach

Reality on the ground Examples from Mahabubnagar illustrating multiple factors that determine good and bad years Higher night temperature (4.5oC) from Nov. –Dec, 1997 resulted in severe outbreak of Helicoverpa Higher sun shine hours (3-4 hrs over normal) during Jan, Feb and March, enhanced the yield level of rice and groundnut and pesticide usage has come down

Z-score (Residuals) Z=(x-mean)/sd

Residuals Probability X

Seasonal Rainfall-JAS: ENSO States mm Years

VII. Weather Manager Tool for Analyzing Weather Data

WeatherManager