“Learning from Farmers Fields to Improve Yield and Profit: a case study for soybean in the US North Central region” by Patricio Grassini at the 2023 Water for Food Global Conference. A recording of the presentation can be found on the conference playlist: https://youtube.com/playlist?list=PLSBeKOIXsg3JNyPowwJj6NDSpx4vlnCYj.

1. Learning from Farmers Fields to Improve
Yield and Profit: a case study for soybean in
the US North Central region
Patricio Grassini, PhD
Sunkist Distinguished Professor of Agronomy
University of Nebraska-Lincoln, USA
With contribution from: Jose Andrade, Juan Ignacio Rattalino Edreira, Shawn Conley,
Spyros Mourtzinis, and other colleagues from the soybean benchmarking projects.

2. ENTENDIENDO CAUSAS DE BRECHAS
Yield
(t/ha/year)
Potential Farmer yield
Yield gap
Adaptaded from van Ittersum et al. (Field Crops Res., 2013)
Poor nutrition?
Poor management?
Pests, diseases?
Causes of yield gaps

3. NPK NP + S, Zn, B
4/22/2014 FAO-WFI Yield Gap Consultation
Photos: P. Grassini, K.G. Cassman, J Mercau, & J. Wendt
Limitations of ‘classic’ agronomic field research
- Limited capacity evaluate multiple factors and their interactions
- Requires a large number of site-years to portray variation in weather and soil.
- Difficulties to bring ‘results to scale’

4. Yield constraints: reality versus percepcions
Comparison of yield constraints as determined via analysis of farmer data (left)
versus those reported as important by local researchers and extensionists (right).
Example for rice & maize in Indonesia
Adapted from Rizzo et al. (submitted to Field Crops Res.)

5. Hipothesis: we can use producer data to identify suites of
management practices that consistently lead to higher yields and/or
input-use efficiencies for given climate and soil type

6. Source: Rattalino Edreira et al., Field Crops Research (2017); Rattalino
Edreira et al., Environmental Research Letters (2018); Mourtzinis et al.,
Field Crops Research (2018); Andrade et al., Agricultural Systems (2022)
Approach based on producer data

7. Case study: narrowing the yield gap in high-yield
soybean systems in the United States
Based on: Andrade J, Mourtzinis S, Rattalino Edreira JI, Conley SP, Gaska J, Kandele HJ, Lindsey LE,
Naeve S, Nelson S, Singh MP, Thompson L, Specht JE, Grassini P (2022) Field validation of a farmer-data
approach to close soybean yield gaps in the US North Central region. Agricultural Systems 200, 103434.

8. About 34 million hectares are annually
planted with soybean in USA,
accounting for ~30% of global
soybean production
USDA-NASS (2011-2015)
The North-Central US region
accounts for ~80% US soybean
production
THE US North Central Region
Average yield represents 70-80% of
the yield potential: there is some
room to produce more!
Grassini et al (2014, 2015); Rattalino Edreira et al. (2017)

9. Data collected from ~8,000 fields planted with soybean in 2014-
2017, covering nearly 200,000 ha

10. Confidential Confidential
Confidential
Survey data

11. Rainfed field in central NE Rainfed field in central IA
Annual rainfall: 610 mm
Soil type: silt loam
Annual rainfall: 890 mm
Soil type: clay loam
IA
Influence of management practices on yield depends on climate and soil type.
Hence, we need to group fields that have same weather-soil background to
identify yield-increasing options for each specific environment

12. Surveyed fields were grouped into 10 climate-soil domains, each TED contains > 100 fields

13. --------------------- RAINFED -------------------------- IRRIGATED -----
Climate-soil type combination

14. Producer field yield (bu/ac)
LOW HIGH
Low-yield
fields
High-yield
fields
?
Number
of
fields
Comparison of high- versus low-yield fields
(Mg/ha)

15. CAUSES OF YIELD GAPS
Planting date
Foliar fungicide
Tillage method
Seed treatment
Row spacing
Frequency (%)
Maturity group
Nematodes
Soybean in USA
Analysis of causes of yield gaps based on farmer field data, grouped according to climate
and soil type, using non-parametric statistical methods
0 20 40 60 80 100

16. Source: Rattalino Edreira et al., AFM 2017
Late sowing reduces yield up
to 33 kg ha-1 per day after late
April, but magnitude of yield
penalty varied across regions
Yield penalty with late sowing

17. TED#1R TED#2R TED#3R
TED#4R TED#5R TED#8I
TED#7R&I TED#6R TED#9I
In many regions,
producers can reduce
costs by adjusting
seeding rate
** estimation based on bag seed price
of US$50 and assuming 140K seeds
per bag and recommended seeding
rate of 310k seeds per ha
For example, farmers in
NE can save 38 US$ per
hectare by following
current seeding rates
recommendations**
Increasing profit through seeding rate adjustment
310k
seeds/ha
Avg=400k

18. REFERENCE
Photo: Laura Thompson
Validation of the farmer-data approach
Comparison between an ‘IMPROVED’ management designed through farmer data analysis (early
sowing, tuning of variety maturity group, foliar fungicide and/or insecticide, lower seeding rate) versus
a ‘REFERENCE’ management (dominant farmer practices) in 100 farmer fields in the US Corn Belt

19. 300 replicated field trials conducted in 2019, 2020 & 2021, located across seven TEDs
Source: Andrade et al., Agricultural Systems, 2022

20. Impact on yield and profit
The improved treatment narrowed the yield gap by 40%, improving net profit (+76 US$ ha-1),
with low economic and environmental risk.
Source: Andrade et al., Agricultural Systems, 2022

21. • Combined use of farmer data and a spatial framework allowed us to
identify causes of yield gaps across a large geographic region with
diversity in climate and soil
• Cost-effective, minimal time for farmers
• For example, a reasonable number of TEDs (10) and number of
fields per TED (ca. 100) was sufficient to identify major yield-
limiting factors across millions of hectares planted with soybean
• Validation of the approach in replicated field trials resulted on
average +277 kg and $76 increase in yield and net profit per
hectare, respectively.
• The approach is generic enough to be applied in other crop
producing regions as long as farmer data and associated
climate/soil databases are available.
• Opportunity to accelerate rates of yield gain, better prioritize Ag
research and extension, and inform policy.
Take-home messages

22. Thanks!
Questions?

23. • Rattalino Edreira et al., 2017. Assessing causes of yield gaps in agricultural areas with diversity in
climate and soils. Agric. For. Meteorol. 247, 170-180.
• Mourtzinis et al., 2018. Sifting and winnowing: Analysis of farmer field data for soybean in the US
North-Central region. Field Crops Res. 221, 130-141.
• Andrade et al., 2019. Assessing the influence of row spacing on soybean yield using experimental and
producer survey data. Field Crops Res. 230, 98-106.
• Rattalino Edreira et al., 2019. From sunlight to seed: Assessing limits to solar radiation capture and
conversion in agro-ecosystems. Agricultural and Forest Meteorology 280, 107775.
• Mourtzinis S, Andrade JF, Grassini P, Rattalino Edreira JI, Kandel H, Naeve S, Nelson K, Helmers M,
Conley SP (2021) Assessing benefits of artificial drainage on soybean yield in the North Central US
region. Agricultural Water Management 243, 106425.
• Rattalino Edreira JI, Mourtzinis S, Azzari G, Andrade JF, Conley SP, Specht JE, Grassini P (2020)
Combining field-level data and remote sensing to understand impact of management practices on
producer yields. Field Crops Research 257, 107932.
• Mourtzinis S, Grassini P, Rattalino Edreira JI, Andrade J, Kyveryga P, Conley S (2020) Assessing
approaches for stratifying producer fields based on biophysical attributes for regional yield-gap
analysis. Field Crops Research 254, 107825.
• Mourtzinis S, Andrade JF, Grassini P, Rattalino Edreira JI, Kandel H, Naeve S, Nelson K, Helmers M,
Conley SP (2021) Assessing benefits of artificial drainage on soybean yield in the North Central US
region. Agricultural Water Management 243, 106425.
• Andrade J, Mourtzinis S, Rattalino Edreira JI, Conley SP, Gaska J, Kandele HJ, Lindsey LE, Naeve S,
Nelson S, Singh MP, Thompson L, Specht JE, Grassini P (2022) Field validation of a farmer-data
approach to close soybean yield gaps in the US North Central region. Agricultural Systems (accepted)
Scientific publications