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

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.Atmosphere 2020, 11, 180 2 of 15 Availability of weather data to run crop simulation models at regional scale applications covering several thousands of square kilometers. In this case, models rely on secondary weather products (spatialized weather variables) where weather variables are measured only at a few sites [8].The measured weather variables can be interpolated (generally each variable separately), which is subsequently used to run the crop model under observed climate conditions. Gridded observational weather data is often used also to adjust bias in regional climate model simulations, which serve as a key tool for climate change impact assessment. However, there are several uncertainties with observed weather data. Many observation stations, particularly in Sub-Saharan Africa, are not equipped to measure certain weather variables (wind speed, relative humidity, open pan evaporation, etc.). Thus, these variables are estimated from measured ones based on equations that have been validated in other regions of the world or at the global scale. The climate data measurement networks, in general, are not compatible, due to differences in instrumentation, sensor height and data logging [9]. In the absence of site-specific data, the following data sources can be identified for spatialized crop yield simulation:

Effects of Recent Climate Change on Maize Yield in Southwest Ecuador

“…LINTUL5 model has been widely used to assess the cropping systems, determine yield gaps and, evaluate the effects of climate change. For example, a study investigated the impact of grid-based time series of climate variables assembled from the same data source and several sources on maize yield in Ghana, Ethiopia, and Nigeria [29]. Another study evaluated heat stress responses on maize crops in Argentina, and Spain [30].…”

Assimilation of Sentinel-2 Estimated LAI into a Crop Model: Influence of Timing and Frequency of Acquisitions on Simulation of Water Stress and Biomass Production of Winter Wheat

“…LINTUL5 is a generic crop model that is able to simulate crop growth under potential, water limited and nitrogen, phosphorus and potassium limited growing conditions for many different annual crops [46]. It has been extensively employed to assess crop responses to various influences [3,[47][48][49][50][51].…”

Simulating Regional Cassava Yield Gap in Nigeria

“…Crop Yp and Yw were simulated from 1995 to 2019 using LINTUL5 (Addiscott and Whitmore, 1991) embedded into the modelling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management) (Gaiser et al, 2013). The applied LINTUL5 version has been used extensively for cropping systems analysis in SSA (Srivastava et al, 2020(Srivastava et al, , 2019(Srivastava et al, , 2017Faye et al, 2018;Trawally et al, 2015).…”