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

Overview Drivers of agriculture risk (climate and non-climate) Analyzing variability at different spatial and temporal scales –Yield variability and spatial scales –Rainfall variability across time 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 Analyais: Mahabubnagar case

… Overview contd… Implications of variability for decision making –Decisions are dynamic –Limitations of using average values 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 “Good” and “bad” years –What are good and bad years? –Methods for analyses: Z-score approach and Percentile Threshold approach Weather Manager: Tool for analyzing weather data

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)

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

Analyzing Variability Across Scales: 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

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 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)

Implications of Variability for Decision Making Station Rainfall Variability Months

Implications of Variability for Decision Making Average Monthly Rainfall

Implications of Variability for Decision Making Seasonal Rainfall Variability (JAS)

JJA JAS Rainfall amount, mm Implications of Variability for Decision Making Exeedence Probability of Rainfall

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 Levels of Spatial Analysis Managing the Full Range of Variability

Spatial levelDecision byCRM OPTIONSOPPORTUNITIES (good events) PlotFamilyChoice of variety, fertilizer dosage, irrigation Family / farm FamilyCrop, enterprise choice CommunityFamilies/ local institutions Use of CPRs, watersheds Region (Sub district) Govt. banks, and other institutions Subsidies, crop insurance, Govt. Schemes DistrictGovt. banks, and other institutions StateGovt. banks, and other institutions, Policy Levels of Spatial Analysis

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

Levels of Spatial Analysis 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% Levels of Spatial Analysis 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

“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 Good and Bad years

Z-score (Residuals) Z=(x-mean)/sd Good and Bad years

Residuals Good and Bad years

Seasonal Rainfall-JAS: ENSO States Good and Bad years

Weather Manager Tool for Analyzing Weather Data

WeatherManager