Abstract
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.
Highlights
IntroductionWeather conditions are one of the important driving factors for analyzing the biophysical processes
Weather conditions are one of the important driving factors for analyzing the biophysical processes.Their influence on these processes are non-linear [1,2], and is dependent on the covariance structure between weather variables [3]
Process-based crop models have extensively been used in the impact assessment of climate change on crop production on regional to the global scale, as they consider the interaction between weather variables and crop management [4]
Summary
Weather conditions are one of the important driving factors for analyzing the biophysical processes. Their influence on these processes are non-linear [1,2], and is dependent on the covariance structure between weather variables [3]. Process-based crop models have extensively been used in the impact assessment of climate change on crop production on regional to the global scale, as they consider the interaction between weather variables and crop management [4]. The quality of crop simulation is frequently constrained by the lack of the required quality of weather data, which varies depending on the source, introducing an additional source of uncertainty in crop simulation results [5,6,7]. Be given to choosing the data source, which otherwise could lead to incorrect estimations that lead to incorrect policy recommendations.
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