Exploring Uncertainties Associated with Scaling Crop Systems Modeling Results from Point to Region KEMIC TEAM Job Kihara (Soil scientist) Jawoo Koo (Crop modeling) Jonathan Hickman (Crop modeling) Charles Vanya (Climatologist) Dilys MacCarthy (Soil scientist) Julius Mangisoni (Economist) Edward Yeboah (Soil scientist) P2R Spontaneous Inception Workshop - H A L F B A K E D -

AFRICA IS LARGER THAN U.S., EUROPE, CHINA, INDIA… COMBINED

Background  Africa is big; points are small. And, we do not have, and won’t have, the complete picture.  Yield estimates being made at sentinel sites (points) need to be aggregated to provide regional/global-scale input data to the economic models.  Scaling-up options are available (or being developed). Many choices and assumptions need to be made; their uncertainties and consequences are not well known.

Research Questions 1.How much uncertainties are we introducing to the point-to-region aggregates, depending on: – Where we simulate (sentinel sites vs. grids, or both) – Choices of soil, climate, and management – How we simulate crop productivity – How we aggregate 2.What are the best options for the reasonable representation of mean and variance in aggregates of point-based estimates?

Objectives 1.To create an independent false “Truth” maize productivity data on 10 km grids for 5-year period 2.To create various point-to-region aggregates generated from using: A.Selected points or uniform grids B.Choices of aggregation methods C.Choices of model input data D.Assumptions of management practices 3.To compare their uncertainties by comparing with the aggregated false “Truth” data. 4.Develop a grid-based crop modeling framework that can be used to test/develop adaptation scenarios for future climate.

Methodology Assumptions 10 km grids adequately represent local variability of soil, climate, and management Study Area Malawi

Generation of grid-level “Truth” of maize productivity on 10 km grids  CERES-Maize + CENTURY  District-level production statistics for  Spatial Production Allocation Model (area/production/yield; disaggregated production statistics on 10 km grids; four levels of input systems)  Gridded soil profile database from HarvestChoice (HWSD + WISE; 10 km grids)  Global fertilizer rate database (60 km grids)  Irrigation extent  Random noise (to take into account model errors) 1

Calibration of locally used maize varieties for four sentinel sites  AfSIS Diagnostic Trials  Millennium Village Project Trials  SIMLESA Project 2

Grid-based characterization of maize production systems in the region For each grid cell, for each of four input systems :  Variety choice  Seasonality + Rainfed/Irrigated  Use of fertilizer: inorganic and organic  Soil fertility (SOM fractions)  … 3

Grid-level DSSAT-based maize yield estimates from various cases, such as… For : 1.Source of soil data 2.Source of climate data 3.Assumption of soil fertility TSBF + HarvestChoice + MVP 4.Seasonality  Rainfed-only  Plus, (hidden) irrigated 2 nd season 5.Fertilizer application rate 6.… 4

Aggregate the point-level data (gridded outputs) various ways 1.Four sentinel sites  District AgMIP protocol; bias correction and match to statistics 2.All sites  District 3.… 5

Notes 1.This study will only focus on crop modeling (no TOA) with one crop model (no APSIM) on current climate (no CC). 2.Outstanding needs  Full understanding of AgMIP Aggregation Protocol  Looking at reality; we’re ambitious (yes)  We do not have budget for this; will need to explore sources to bring members together. 3.Plan  Sep: Straight-out workplan  Oct: Present at the Rome meeting, seeking feedback and possible contribution to the global-scale aggregation team  Nov: Get all the data ready  Feb: First round of results ready for review  Apr: Finalize the study  Oct: Publication (TBD)