Climate variability in areas of the world with high production of soya beans and corn: its relationship to crop yields

Llano, María Paula; Vargas, Walter M.; Naumann, Gustavo

doi:10.1002/met.270

Cited by 10 publications

(13 citation statements)

References 15 publications

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“…According to the agricultural statistics supplied by the United Nations Food and Agriculture Organization [27], 65% of the world production of corn and more than 90% of the world production of soybeans occurs in Argentina, Brazil, the United States and China. The productivity of these crops is expected to decrease in the extensive plains located in middle and subtropical latitudes of South-America (e.g., Brazil and Argentina), leading to a reduction in the worldwide productivity of cattle farming and having adverse consequences for global food security [28,29].…”

Section: Study Area Datasets and Methodsmentioning

confidence: 99%

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Carrão

Naumann

Dutra

et al. 2018

Climate

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Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF) seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for Latin America, and their skill is evaluated over the hindcast period 1981-2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3-and 6-month SPI were evaluated). Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin America are obtainable from improvements in near-real-time precipitation observations for the region. In the longer term, improvements in precipitation forecast skill from dynamical models, like the fifth generation of the ECMWF seasonal forecasting system, will be essential in this effort.

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Section: Study Area Datasets and Methodsmentioning

confidence: 99%

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Carrão

Naumann

Dutra

et al. 2018

Climate

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“…Fundamentally, drought is a temporary water supply deficit relative to some long-term average condition. Dracup and Lee (1980) and Wilhite and Glantz (1985) proposed a drought 2010; Llano et al, 2012;Martnez-Fernndez et al, 2015;Vyas et al, 2015). Additionally, it diminishes forest productivity and increases fire hazard (Caccamo et al, 2011;Hofer et al, 2012).…”

Section: Introductionmentioning

confidence: 91%

“…To evaluate the spatio-temporal consistency of the ESSMI and ECV SM data set, we analyze a time-series of index values for a region in Bahia (northeast Brazil), as similar as Lloyd- Hughes and Saunders (2002) and Lloyd-Hughes (2012). Finally, to evaluate the suitability and potential of the ESSMI for assessing agricultural drought impacts, we tested it against agricultural productivity, as similar as Narasimhan and Srinivasan (2005), Penalba et al (2007), Kumar et al (2009), Nagarajan (2003, Rhee et al (2010), Llano et al (2012), Martnez-Fernndez et al (2015) and Vyas et al (2015), to cite but a few.…”

Section: Introductionmentioning

confidence: 99%

An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data

Carrão

Russo

Sepulcre-Cantó

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

138

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a b s t r a c tWe propose a simple, spatially invariant and probabilistic year-round Empirical Standardized Soil Moisture Index (ESSMI) that is designed to classify soil moisture anomalies from harmonized multi-satellite surface data into categories of agricultural drought intensity. The ESSMI is computed by fitting a nonparametric empirical probability density function (ePDF) to historical time-series of soil moisture observations and then transforming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry soil conditions, whereas positive values indicate wet soil conditions. Drought intensity is defined as the number of negative standard deviations between the observed soil moisture value and the respective normal climatological conditions. To evaluate the performance of the ESSMI, we fitted the ePDF to the Essential Climate Variable Soil Moisture (ECV SM) v02.0 data values collected in the period between January 1981 and December 2010 at South-Central America, and compared the root-mean-square-errors (RMSE) of residuals with those of beta and normal probability density functions (bPDF and nPDF, respectively). Goodness-of-fit results attained with time-series of ECV SM values averaged at monthly, seasonal, half-yearly and yearly timescales suggest that the ePDF provides triggers of agricultural drought onset and intensity that are more accurate and precise than the bPDF and nPDF. Furthermore, by accurately mapping the occurrence of major drought events over the last three decades, the ESSMI proved to be spatio-temporal consistent and the ECV SM data to provide a well calibrated and homogenized soil moisture climatology for the region. Maize, soybean and wheat crop yields in the region are highly correlated (r > 0.82) with cumulative ESSMI values computed during the months of critical crop growing, indicating that the nonparametric index of soil moisture anomalies can be used for agricultural drought assessment.

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“…The summer warming trend from 1891-1936 adversely affected crop yields, while a cooling trend accompanied by increased summer rainfall decreased variability in yield and accounted for a 20% increase in yield from 1936-1972 [1]. Crop yields during the period of 1980-2007 were strongly correlated with the occurrence of meteorological drought and maximum daily temperature during the grain filling and reproductive growth period [2,3]. Soil moisture that is either higher or lower than normal conditions can cause crop water stresses, which are harmful for crop growth over shorter spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series

2016

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Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000-2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmental factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. The spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.

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Climate variability in areas of the world with high production of soya beans and corn: its relationship to crop yields

Cited by 10 publications

References 15 publications

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data

Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series

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