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Robustly forecasting maize yields in Tanzania based on climatic predictors
Seasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yiel...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665066/ https://www.ncbi.nlm.nih.gov/pubmed/33184303 http://dx.doi.org/10.1038/s41598-020-76315-8 |
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author | Laudien, Rahel Schauberger, Bernhard Makowski, David Gornott, Christoph |
author_facet | Laudien, Rahel Schauberger, Bernhard Makowski, David Gornott, Christoph |
author_sort | Laudien, Rahel |
collection | PubMed |
description | Seasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill. |
format | Online Article Text |
id | pubmed-7665066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76650662020-11-16 Robustly forecasting maize yields in Tanzania based on climatic predictors Laudien, Rahel Schauberger, Bernhard Makowski, David Gornott, Christoph Sci Rep Article Seasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill. Nature Publishing Group UK 2020-11-12 /pmc/articles/PMC7665066/ /pubmed/33184303 http://dx.doi.org/10.1038/s41598-020-76315-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Laudien, Rahel Schauberger, Bernhard Makowski, David Gornott, Christoph Robustly forecasting maize yields in Tanzania based on climatic predictors |
title | Robustly forecasting maize yields in Tanzania based on climatic predictors |
title_full | Robustly forecasting maize yields in Tanzania based on climatic predictors |
title_fullStr | Robustly forecasting maize yields in Tanzania based on climatic predictors |
title_full_unstemmed | Robustly forecasting maize yields in Tanzania based on climatic predictors |
title_short | Robustly forecasting maize yields in Tanzania based on climatic predictors |
title_sort | robustly forecasting maize yields in tanzania based on climatic predictors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665066/ https://www.ncbi.nlm.nih.gov/pubmed/33184303 http://dx.doi.org/10.1038/s41598-020-76315-8 |
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