Comparing empirical and survey-based yield forecasts in a dryland agro-ecosystem

Yi Zhao, Noemi Vergopolan, Katherine R Baylis, Jordan Blekking, Kelly Caylor, Tom Evans, Stacey Giroux, Justin Sheffield, Lyndon Estes

Research output: Contribution to journalArticle

Abstract

Accurate crop yield forecasts before harvest are crucial for providing early warning of agricultural losses, so that policy-makers can take steps to minimize hunger risk. Within-season surveys of farmers’ end-of-season harvest expectations are one important method governments use to develop yield forecasts. Survey-based methods have two potential limitations whose effects are poorly understood. First, survey-based forecasts may be subject to errors and biases in the response data. For example, the weather variables that most impact yields may not be the same as those that farmers consider when shaping their yield expectations, thereby undermining forecast accuracy. Secondly, surveys are typically conducted late in the growing season, giving the government less advance notices of potential crop failures or low yields, and are costly to implement. Here we investigate these limitations within the context of Zambia's annual Crop Forecast Survey (CFS). Concerning the first limitation, we analyzed the differences between CFS-predicted yields and reported yields collected by Post Harvest Surveys, and found that excess rainfall during the planting stage was more important to the actual yield than to farmers’ yield forecasts. For the second limitation, we evaluated whether a simple empirical yield forecast model could produce earlier and more accurate yield forecasts than the CFS. A random forest model using weather variables, soil texture, and soil pH as predictors were able to produce yield forecasts at the same or higher accuracy since the planting season.

Original languageEnglish (US)
Pages (from-to)147-156
Number of pages10
JournalAgricultural and Forest Meteorology
Volume262
DOIs
StatePublished - Nov 15 2018

Fingerprint

arid lands
ecosystems
ecosystem
crop
crops
farmers
forecast
weather
planting
Zambia
hunger
harvest date
soil texture
crop yield
soil pH
growing season
rain
rainfall

Keywords

  • Empirical modeling
  • Yield forecast

ASJC Scopus subject areas

  • Forestry
  • Global and Planetary Change
  • Agronomy and Crop Science
  • Atmospheric Science

Cite this

Comparing empirical and survey-based yield forecasts in a dryland agro-ecosystem. / Zhao, Yi; Vergopolan, Noemi; Baylis, Katherine R; Blekking, Jordan; Caylor, Kelly; Evans, Tom; Giroux, Stacey; Sheffield, Justin; Estes, Lyndon.

In: Agricultural and Forest Meteorology, Vol. 262, 15.11.2018, p. 147-156.

Research output: Contribution to journalArticle

Zhao, Y, Vergopolan, N, Baylis, KR, Blekking, J, Caylor, K, Evans, T, Giroux, S, Sheffield, J & Estes, L 2018, 'Comparing empirical and survey-based yield forecasts in a dryland agro-ecosystem', Agricultural and Forest Meteorology, vol. 262, pp. 147-156. https://doi.org/10.1016/j.agrformet.2018.06.024
Zhao, Yi ; Vergopolan, Noemi ; Baylis, Katherine R ; Blekking, Jordan ; Caylor, Kelly ; Evans, Tom ; Giroux, Stacey ; Sheffield, Justin ; Estes, Lyndon. / Comparing empirical and survey-based yield forecasts in a dryland agro-ecosystem. In: Agricultural and Forest Meteorology. 2018 ; Vol. 262. pp. 147-156.
@article{c9820d7effff41f6ae76f0faa86bf494,
title = "Comparing empirical and survey-based yield forecasts in a dryland agro-ecosystem",
abstract = "Accurate crop yield forecasts before harvest are crucial for providing early warning of agricultural losses, so that policy-makers can take steps to minimize hunger risk. Within-season surveys of farmers’ end-of-season harvest expectations are one important method governments use to develop yield forecasts. Survey-based methods have two potential limitations whose effects are poorly understood. First, survey-based forecasts may be subject to errors and biases in the response data. For example, the weather variables that most impact yields may not be the same as those that farmers consider when shaping their yield expectations, thereby undermining forecast accuracy. Secondly, surveys are typically conducted late in the growing season, giving the government less advance notices of potential crop failures or low yields, and are costly to implement. Here we investigate these limitations within the context of Zambia's annual Crop Forecast Survey (CFS). Concerning the first limitation, we analyzed the differences between CFS-predicted yields and reported yields collected by Post Harvest Surveys, and found that excess rainfall during the planting stage was more important to the actual yield than to farmers’ yield forecasts. For the second limitation, we evaluated whether a simple empirical yield forecast model could produce earlier and more accurate yield forecasts than the CFS. A random forest model using weather variables, soil texture, and soil pH as predictors were able to produce yield forecasts at the same or higher accuracy since the planting season.",
keywords = "Empirical modeling, Yield forecast",
author = "Yi Zhao and Noemi Vergopolan and Baylis, {Katherine R} and Jordan Blekking and Kelly Caylor and Tom Evans and Stacey Giroux and Justin Sheffield and Lyndon Estes",
year = "2018",
month = "11",
day = "15",
doi = "10.1016/j.agrformet.2018.06.024",
language = "English (US)",
volume = "262",
pages = "147--156",
journal = "Agricultural and Forest Meteorology",
issn = "0168-1923",
publisher = "Elsevier",

}

TY - JOUR

T1 - Comparing empirical and survey-based yield forecasts in a dryland agro-ecosystem

AU - Zhao, Yi

AU - Vergopolan, Noemi

AU - Baylis, Katherine R

AU - Blekking, Jordan

AU - Caylor, Kelly

AU - Evans, Tom

AU - Giroux, Stacey

AU - Sheffield, Justin

AU - Estes, Lyndon

PY - 2018/11/15

Y1 - 2018/11/15

N2 - Accurate crop yield forecasts before harvest are crucial for providing early warning of agricultural losses, so that policy-makers can take steps to minimize hunger risk. Within-season surveys of farmers’ end-of-season harvest expectations are one important method governments use to develop yield forecasts. Survey-based methods have two potential limitations whose effects are poorly understood. First, survey-based forecasts may be subject to errors and biases in the response data. For example, the weather variables that most impact yields may not be the same as those that farmers consider when shaping their yield expectations, thereby undermining forecast accuracy. Secondly, surveys are typically conducted late in the growing season, giving the government less advance notices of potential crop failures or low yields, and are costly to implement. Here we investigate these limitations within the context of Zambia's annual Crop Forecast Survey (CFS). Concerning the first limitation, we analyzed the differences between CFS-predicted yields and reported yields collected by Post Harvest Surveys, and found that excess rainfall during the planting stage was more important to the actual yield than to farmers’ yield forecasts. For the second limitation, we evaluated whether a simple empirical yield forecast model could produce earlier and more accurate yield forecasts than the CFS. A random forest model using weather variables, soil texture, and soil pH as predictors were able to produce yield forecasts at the same or higher accuracy since the planting season.

AB - Accurate crop yield forecasts before harvest are crucial for providing early warning of agricultural losses, so that policy-makers can take steps to minimize hunger risk. Within-season surveys of farmers’ end-of-season harvest expectations are one important method governments use to develop yield forecasts. Survey-based methods have two potential limitations whose effects are poorly understood. First, survey-based forecasts may be subject to errors and biases in the response data. For example, the weather variables that most impact yields may not be the same as those that farmers consider when shaping their yield expectations, thereby undermining forecast accuracy. Secondly, surveys are typically conducted late in the growing season, giving the government less advance notices of potential crop failures or low yields, and are costly to implement. Here we investigate these limitations within the context of Zambia's annual Crop Forecast Survey (CFS). Concerning the first limitation, we analyzed the differences between CFS-predicted yields and reported yields collected by Post Harvest Surveys, and found that excess rainfall during the planting stage was more important to the actual yield than to farmers’ yield forecasts. For the second limitation, we evaluated whether a simple empirical yield forecast model could produce earlier and more accurate yield forecasts than the CFS. A random forest model using weather variables, soil texture, and soil pH as predictors were able to produce yield forecasts at the same or higher accuracy since the planting season.

KW - Empirical modeling

KW - Yield forecast

UR - http://www.scopus.com/inward/record.url?scp=85049808722&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049808722&partnerID=8YFLogxK

U2 - 10.1016/j.agrformet.2018.06.024

DO - 10.1016/j.agrformet.2018.06.024

M3 - Article

AN - SCOPUS:85049808722

VL - 262

SP - 147

EP - 156

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

SN - 0168-1923

ER -