Next Article in Journal
Evidence of a Diurnal Cycle in Precipitation over the Southern Ocean as Observed at Macquarie Island
Previous Article in Journal
Source Identification of Trace Elements in PM2.5 at a Rural Site in the North China Plain
Open AccessArticle

The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations

1
Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany
2
Joint Research Centre, European Commission, Via Enrico Fermi 2749, 21027 Ispra, Italy
3
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(2), 180; https://doi.org/10.3390/atmos11020180
Received: 31 December 2019 / Revised: 31 January 2020 / Accepted: 4 February 2020 / Published: 9 February 2020
(This article belongs to the Special Issue Climate-Water-Food Nexus)
High-resolution and consistent grid-based climate data are important for model-based agricultural planning and farm risk assessment. However, the application of models at the regional scale is constrained by the lack of required high-quality weather data, which may be retrieved from different sources. This can potentially introduce large uncertainties into the crop simulation results. Therefore, in this study, we examined the impacts of grid-based time series of weather variables assembled from the same data source (Approach 1, consistent dataset) and from different sources (Approach 2, combined dataset) on regional scale crop yield simulations in Ghana, Ethiopia and Nigeria. There was less variability in the simulated yield under Approach 1, ranging to 58.2%, 45.6% and 8.2% in Ethiopia, Nigeria and Ghana, respectively, compared to those simulated using datasets retrieved under Approach 2. The two sources of climate data evaluated here were capable of producing both good and poor estimates of average maize yields ranging from lowest RMSE = 0.31 Mg/ha in Nigeria to highest RMSE = 0.78 Mg/ha under Approach 1 in Ghana, whereas, under Approach 2, the RMSE ranged from the lowest value of 0.51 Mg/ha in Nigeria to the highest of 0.72 Mg/ha in Ethiopia under Approach 2. The obtained results suggest that Approach 1 introduces less uncertainty to the yield estimates in large-scale regional simulations, and physical consistency between meteorological input variables is a relevant factor to consider for crop yield simulations under rain-fed conditions.
Keywords: maize; crop model; weather data sources; Sub-Saharan Africa maize; crop model; weather data sources; Sub-Saharan Africa
MDPI and ACS Style

Srivastava, A.K.; Ceglar, A.; Zeng, W.; Gaiser, T.; Mboh, C.M.; Ewert, F. The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations. Atmosphere 2020, 11, 180.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop