Scalable yield gap analysis

David B. Lobell

Associate Professor, Department of Environmental Earth System ScienceAssociate Director, Center on Food Security and the Environment

dlobell@stanford.edu

• In 2004, Ivan and I gave a talk in El Batan about how remote sensing could be really useful

• A lot has changed since, but these resources are still generally underutilized

• Technology has advanced a little slower than many of us expected, but the pace of progress seems to be picking up

When is remote sensing useful?Type of cropping system Applications that are likely useful

1) Low input, subsistence systems

•Providing basic statistics on area and production•Early warning of shortfalls•Tracking impacts of interventions

2) High input, low input use efficiency •Real-time management assistance

•Yield gap analysis3) High input, high input

use efficiency

The goals of yield gap analysis

To answer questions such as:

•How big are exploitable yield gaps?

•What key factors cause yield gaps?

•On what practices should research and extension efforts focus for biggest yield gain?

•Which fields are especially good or bad for a particular crop or variety?

(From Van Ittersum et al. 2013)

Scalable yield gap analysis

What do I mean by scalable?

•Can be rapidly applied in a new area•Does not rely on field samples to calibrate yield estimates

Our current approach is a 4-step process1. Yield estimation for individual fields for 3+ years2. Analysis of the temporal consistency of spatial patterns3. Comparison of average yields with other readily available

spatial datasets, such as on soil types, roads, and irrigation infrastructure

4. Targeted field surveys that focus on areas with highest and lowest average yields

Daily time step crop model (e.g. APSIM,

Hybrid-Maize)

Daily Weather

Data

Available satellite

images for year T

100+ sets of crop model parameters (sow date, density,

fertilizer, etc.)

Crop Classification

Maps

Surface Reflectance

Veg. Indices (WDRVI)

Simulated yields and

veg. indices (N > 100)

Regressions that link VIs on image date(s) to final yields

Annual maps of crop yields

Data inputs

Crop Model

Prescribed Parameters

Outputs / Intermediate Variables

1. Automated yield estimation

~4.0 ton/Ha

~7.0 ton/Ha

1993-94

1999-00

2000-01

2001-02

2002-03

Farmer Reported Yield (ton ha-1)

4 5 6 7 8

Mea

n S

atel

lite-

Bas

ed Y

ield

(ton

ha

-1)

4

5

6

7

8

R2 = 0.78rms = 0.37 ton ha-1

1:1 line

~4.0 ton/Ha

~7.0 ton/Ha

1 km

Wheat yield estimates derived from Landsat in Yaqui Valley, Mexico

Sangrur

MansaBhatinda

MogaFaridkot

Mukstar

2002 Wheat Yield in Punjab (Mg/ha) 2002 Planting Date in Punjab

2.5

5.5

Oct 15

Dec 25

Sangrur

MansaBhatinda

MogaFaridkot

Mukstar

5 km

0 25 50km

4.0 Nov 19

Maize yields in North China Plain

R2 = 0.48

Quzhou, Hebei Province

Our current approach is a 4-step process1. Yield estimation for individual fields for 3+ years2. Analysis of the temporal consistency of spatial patterns3. Comparison of average yields with other readily available

spatial datasets, such as on soil types, roads, and irrigation infrastructure

4. Targeted field surveys that focus on areas with highest and lowest average yields

Outline

Not all yield differences are persistent

Measures of yield persistence can help identify how much of overall yield gap is driven by persistent factors

Measures of yield persistence can help identify how much of overall yield gap is driven by persistent factors

Estimated maize yields (t/ha) in part of Madison, Nebraska20032002 2004 2005

Can also readily do things like look at between vs. within field yield variation

20082007 2010

2011 2012

2009

Estimated maize yields (t/ha) in part of Madison, Nebraska

Our current approach is a 4-step process1. Yield estimation for individual fields for 3+ years2. Analysis of the temporal consistency of spatial patterns3. Comparison of average yields with other readily available

spatial datasets, such as on soil types, roads, irrigation infrastructure, crop rotation, etc.

4. Targeted field surveys that focus on areas with highest and lowest average yields

Outline

Some factors typically emerge as important, others not

Wheat yields in Indian Punjab vs. distance to roads or canals

Lobell et al. 2010, Field Crops Research

Our current approach is a 4-step process1. Yield estimation for individual fields for 3+ years2. Analysis of the temporal consistency of spatial patterns3. Comparison of average yields with other readily available

spatial datasets, such as on soil types, roads, and irrigation infrastructure

4. Targeted field surveys that focus on areas with highest and lowest average yields

Note: this is the most time consuming step, but it comes last and is guided by the first three.

Outline

Farmer surveys in Quzhou China

My questions:The next five years should be very exciting, but there is finite time

and resources. So…

• How much interest exists at CIMMYT for yield gap analysis, or is there more interest on other uses, like real-time management, estimating crop areas, and impact evaluation?

• Is it better to continue in ‘research mode’ where estimates are made for areas with specific questions and projects in mind, or in “public good mode” where we simply try to map all wheat and maize systems of the world and make it available to researchers (and farmers)?

Acknowledgements

Most of this work was inspired by and done in collaboration with Ivan Ortiz-Monasterio

Funding from NASA, Fundacion Sonora, Stanford University

Students/ Research Assistants: Adam Sibley, Yi Zhao, Nancy Thomas, Christopher Seifert

Thanks for your attention!