The biophysical effect of changing climate patterns on agriculture and crop management has been largely studied [1
]. Moreover, the impact of this change is known to be higher in developing countries and small farmers relegated to subsistence rainfed agriculture (no irrigation) [3
]. Therefore, it is imperative for countries to study the changes in their yield values in actual climates, and simulate their yields in future climates [4
]. In fact, 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
], and often are studied in relation to food security [6
]. However, there are also studies carried out on crops of specific economical interest in a country, or those used as foodstuffs, such as sugarcane or others [7
]. A comprehensive review of the effects of climate change in agricultural crops is provided in [8
Some studies have been geared toward the projection of maize in countries or regions, for instance, in the case of the Latin American region, a seminal 2003 article by Jones and Thornton [9
] is often used as an example. In this article, the authors focuses on the impact that climate change will have on maize for Africa and Latin America, country by country up to 2055, using the Crop Estimation through Resource and Environment Synthesis crop model (CERES)-Maize models. The authors project an overall 10% decrease in maize yield in 2055, but suggest that this be counteracted with technological interventions and selective plant breeding. For the case of maize yield in Panama, Jones and Thornton [9
] calculate a loss of 238 kg ha
which represents a loss of more than 14,000 tonnes in 2055. In the work by Ruane et al. [10
], a clearer picture is presented about Panama and the production of maize. The authors focus on Los Santos and investigate the impact on maize yield in three distinct time periods: near-term (2005–2034), mid-century (2040–2069), and end-of-century (2070–2099) using an ensemble of 16 GCM models and the CERES-Maize model in two different scenarios, one with high emissions A2 (comparable forcing as RCP8.5) and low emission B1 (comparable to RCP 4.5). The resulting yields are as follows (for A2 and B1, respectively), decrease of 0.5% and 0.1% for near-term, increase of 2.4% and decrease of 0.8% for mid-century, and increase of 4.5% and 1.5% for end-of-century.
Besides these process-based models, other developments are based on statistical crop yield models, often called empirical models. In terms of their output, both models are comparable having strong overlap in their results [11
]. The first type of model often include the effects of CO
which can be related to warming, while in statistical models this is not accounted for [11
]. Kogo et al. [14
] states that while simulation and process-based models need to be validated, a regression model is adjusted from real agronomic yields, thus reflecting the true phenomenon. In Holzkämper et al. [15
], the effects of temperature and precipitation has been assessed with a statistical crop model for maize. In Holzkämper et al. [16
], the analysis is extended to include both process-based and statistical crop models for the determination of climate impact on maize. Specifically in regards to statistical methods, Shi et al. highlights the issues existing in the crop yield sensitivity to climate change using statistical models, which include defining the extent of spatial and temporal scales, trend removal, and removing co-linearity in models. Some of this issues are also discussed in [17
The case of the future climate in The Republic of Panama has been addressed by many researchers and with different models. However, a considerable number of studies have focused in precipitation at the End-of-Century data using the MRI-AGCM model [18
]. Most of these studies indicate that in the future, precipitation will increase in the central and eastern parts of Panama from May to November, corresponding to the rainy season. The increase in precipitation in most regions can be attributed to the increased transport of water vapor originating in the Caribbean Sea, which converges on Panama.
Compared to the amount of work dedicated to predictive climate studies for Panama, less work has been done regarding the implications of these future climate changes. Early work by Espinosa et al. [22
] assessed the impact of climate change impacts on the water resources of Panama for three regions in La Villa (Los Santos Province), Chiriquí, and Chagres river basins. In [23
], the climate change is assessed for agricultural development for the highlands of Western Panama in Chiriquí Province. More interestingly, Garcia et al. [24
] focused on climate anomalies found on the projections for La Villa River Basin for the years 2050 and 2070 using data from the WorldClim Meteorological Database.
In fact, aside from Ruane’s efforts using a process-based model for maize in the Azuero Region, little research has been carried out to evaluate climatic scenarios and relate their implications on crop yields. In response to this reality, the objectives of this work are as follows. (1) Explore the meteorological predictions data set provided by the MRI-ACGM model. (2) Study the projected precipitation and temperature for the RCP8.5 scenarios for four different Sea Surface Temperature ensembles. (3) Explore the impact of precipitation and temperature in the Azuero Region using a statistical crop model and study its implications for maize production in different planting dates. (4) Provide bias-corrected estimates for key meteorological variables for further use.
It is well known that climate variability translates to an important change in the rules of the game for crop production. This obviously represents enormous challenges for producers, farmers, and government, but also for other actors in academia that might need to provide solutions for planning and mitigation. In consequence, it implies that there is a necessity to make deep adjustments in the agricultural activity for its future subsistence [25
]. In other words, understanding that climate change implies a clear and direct risk for food security at the regional and global levels. For this reason, there is a need to study climate projections from models and their short-, medium, and long-term for the agricultural sector [12
]. For The Republic of Panama, this risk extends to understanding the changes that might occur in water critical activities in Panama (see The Panama Canal operation, hydroelectric power generation, and, of greater importance to our study, agriculture) [20
In this work, projected variables precipitation and temperature, from the MRI-AGCM 3.2 model, were studied in the Azuero region. Results confirmed that an increase in precipitation will be appreciated in the region at the end of the century [50
]. More specifically, daily precipitation is projected to go up on average >15mm and temperatures will rise above 26
C and up to 36
C. Regarding localities, future precipitation projections are adjusted for localities north of Azuero region with respect to the C0 SST-ensemble. Future precipitation projections are adjusted for the localities south of Azuero with respect to SST-ensembles C1, C2, and C3.
Simple correction was used to reduce the yield on the present time projections (SPA). The Delta Method was used to reduce the biases of the future climate estimates (SFA), for both variables precipitation and temperature. In the case of the maximum temperature, the values increase with respect to the corrected ones. The regression model tends to underestimate the maximum projected temperature and overestimate the minimum projected temperature. Not surprisingly, the lowest yields are found for all SST-ensemble for the later planting date. More interestingly, the yield model showed that for the C0, C2, and C3 SST-ensembles, the yield will double, while for the C1 SST-ensemble predicts, an average of 5% decrease for the period. This suggests that while the yield model shows that the production will be doubled, this change is sensitive to the future sea surface temperature distribution.
As future work, in terms of the agronomic model, a longer time series could be used to calculate maize yield. In this new model, it would be sensible to include the number of plants that exist at harvest time, to adjust the expected yield to the number of maize plants per hectare. This number now amounts to 65,000 per hectare and is a crucial factor to determine the yield.
Another important point would be to have data on irrigated maize, to be able to have a model that predicts the yield without water deficiencies. This will help to further separate the effect of rainfall and temperature, since for the regression model for the Azuero Region, rainfall captures more the variability of the response of the grain yield than temperature. This is in part due to its greater variation. In that same vein a model can incorporate a different treatment of variables, for instance de-trending of dependent and independent variables, log-transformation of the yield variable), to provide additional information about on how to determine the independent effect of temperature and precipitation on the crop yield, on present and future time.
Precipitation is well studied phenomenon in Panama, mostly for ensuring its availability for the functioning of the Panama Canal and the water cycle on the Canal watershed. However, other variables that are important for crop growth, for instance, VPD, solar radiation, and UV rays, which are important for maize, have not been projected in the end-of-century period. These variables should be incorporated into newer models as they become available to the research community.
Moreover, it is known that the CO
fertilization effect [51
] is not well managed in statistical crop models [14
]. Subsequent models need to include this component either by making an ensemble of both process-based and statistical crop models as suggested by Roberts et al. [13
] and Lobell et al. [11
], subtracting the effect of CO
from having yields under different RCPs [10
], calculating it from a multi model comparison [5
], or estimating it on-site as are done with the rain-forest in Barro Colorado Island in Panama [52
Finally, it is important to state that this study and similar studies are of great relevance; The Azuero Region alone is responsible for over 95% of the maize grown in the country, with 18,000 hectares produced annually and a total production of 45,000 tons of grains per year [49
]. Therefore, all mitigation efforts and efforts to understand future climates and their implications are very for the sovereignty and food security of Panama.