Abstract
In this article, we evaluate the impact of temperature and precipitation at the end of the 21st century (2075–2099) on the yield of maize in the Azuero Region in Panama. Using projected data from an atmospheric climate model, MRI-ACGM 3.2S, the study variables are related to maize yield (t ha−1) under four different sea surface Temperature (SST) Ensembles (C0, C1, C2, and C3) and in three different planting dates (21 August, 23 September, and 23 October). In terms climate, results confirm the increase in temperatures and precipitation intensity that has been projected for the region at the end of the century. Moreover, differences are found in the average precipitation patterns of each SST-ensemble, which leads to difference in maize yield. SST-Ensembles C0, C1, and C3 predict a doubling of the yield observed from baseline period (1990–2003), while in C1, the yield is reduced around 5%. Yield doubling is attributed to the increase in rainfall, while yield decrease is related to the selection of a later planting date, which is indistinct to the SST-ensembles used for the calculation. Moreover, lower yields are related to years in which El Niño Southerm Oscilation (ENSO) are projected to occur at the end of century. The results are important as they provide a mitigation strategy for maize producers under rainfed model on the Azuero region, which is responsible for over 95% of the production of the country.
Highlights
The biophysical effect of changing climate patterns on agriculture and crop management has been largely studied [1,2]
Maize cultivation in Panama is concentrated in the eastern portion of the Azuero Peninsula that extends into the Pacific from the south of the country [25]
A comparison was made between the four different sea surface Temperature (SST)-ensembles schemes for the variables precipitation (Figure 3A) and average daily temperature (Figure 3B) and both extremes, minimum (Figure 3C) and maximum temperatures (Figure 3D), for all SST-ensembles
Summary
The biophysical effect of changing climate patterns on agriculture and crop management has been largely studied [1,2]. It is imperative for countries to study the changes in their yield values in actual climates, and simulate their yields in future climates [4]. This process often is done via biophysical models, agro-ecological models, statistical analysis models, and global gridded crop models. These models are done to provide worldwide estimates, usually for selected crops (high food energy cereals) such as maize, wheat, rice, and soybean [4,5], and often are studied in relation to food security [6]. There are studies carried out on crops of specific economical interest in a country, or those used as foodstuffs, such as sugarcane or others [7]
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