Forecasting soybean production to enhance climate services for Agriculture in Argentina

Thomasz, Esteban Otto; Corfield, Kevin; Vilker, Ana Silvia; Osman, Marisol

doi:10.1016/j.cliser.2023.100341

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…According to Kumagai [15], water supply plays a particularly important role in soybean production, and soybean yields in northern Japan were influenced by precipitation levels and distribution across years and experimental sites. Thomasz et al [54] analyzed the relationship between soil water content and soybean yields in 28 agricultural districts in Argentina. The data provided by local weather stations were used in correlation and regression analyses and to forecast soybean yields.…”

Section: Discussionmentioning

confidence: 99%

The Effect of Sowing Date on the Growth and Yield of Soybeans Cultivated in North-Eastern Poland

Fordoński,

Okorski,

Olszewski

et al. 2023

Agriculture

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Soybean yields are influenced by numerous factors, including environmental conditions, location, and agricultural practices. Sowing date affects plant growth, development, and yields, and it plays a particularly important role in soybean cultivation. The optimal sowing date should be selected based on soil temperature, precipitation, and rainfall distribution in a given region. The aim of this study was to determine the effect of various sowing dates (I—early, II—optimal, III—late) on the time from sowing to emergence of soybean seedlings, length of the growing season, morphological traits of soybean plants, yield components, and seed yields of soybeans grown in north-eastern Poland. Sowing date considerably affected the time from sowing to the emergence of soybean seedlings and seed yields. In north-eastern Poland, soybeans should be sown in the first half of May to minimize the risk of ground frost damage, which can occur even in late May. Sowing date also influenced soybean yields. In north-eastern Poland (Region of Warmia and Mazury), yields were maximized when soybeans were sown late (in mid-May), which was decisively influenced by climatic conditions, mainly temperature. The linear regression analysis revealed that the length of the growing season was correlated with the seed yields of soybeans sown on different dates.

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Section: Discussionmentioning

confidence: 99%

The Effect of Sowing Date on the Growth and Yield of Soybeans Cultivated in North-Eastern Poland

Fordoński,

Okorski,

Olszewski

et al. 2023

Agriculture

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“…The 2015/2016 devastating drought in southern Africa affected about 40 million people and resulted in a cereal deficit of 9.3 million tons and more than 643,000 livestock deaths (Matlou et al, 2021). According to Thomasz et al (2023), Argentina produces 50 million tons of soybean annually and is the third largest in the world after the USA and Brazil, and water deficit during critical period and excess during harvest affect output. The 1997/98 El Niño drought had a major impact on New Zealand agriculture and resulted in a loss of $618 million to the GDP (Salinger & Porteous, 2014).…”

Section: Agricultural Sectormentioning

confidence: 99%

Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere

Phakula,

Landman,

Engelbrecht

2024

Meteorological Applications

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Subseasonal‐to‐seasonal (S2S) prediction has gained momentum in the recent past as a need for predictions between the weather forecasting timescale and seasonal timescale exists. The availability of S2S databases makes prediction and predictability studies possible over all the regions of the globe. Most S2S studies are, however, relevant to the northern hemisphere. In this review, the S2S literature relevant to the southern hemisphere (SH) are presented. Predictive skill, sources of predictability, and the application of S2S predictions are discussed. Indications from the subseasonal predictability studies for the SH regions suggest that predictive skill is limited to 2 weeks in general, particularly for temperature and rainfall, which are the variables most frequently investigated. However, temperature has enhanced skill compared to rainfall. More S2S prediction studies that include the quantification of the sources of predictability and the identification of windows of opportunity need to be conducted for the SH, particularly for the southern African region. The African continent is vulnerable to weather‐ and climate‐related disasters, and S2S forecasts can assist in alleviating the risk of such disasters.

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“…In addition, Asseng et al [37] and Wall et al [38] have shown a robust linear relationship between wheat yield and seasonal mean temperature. Other crop yield models for Argentina used additional variables like soil moisture [30], large-scale climate indices like the El Niño-Southern Oscillation index [21,39] or remotely sensed quantities like land [35]. Wheat yield on municipality level was aggregated to regional and national level by calculating the weighted mean yield with weights being the harvested area.…”

Section: Temperature and Precipitation Datamentioning

confidence: 99%

“…Given that Argentina is a major wheat producer and exporter in South America, contributing around 60%-70% of total South American wheat grain output [26,27], we use Argentina as a test country and wheat as the model crop to analyze if seasonal forecast performance can be improved and used within a crop yield model when a MME is built systematically. Like previous studies [6,10,[28][29][30], we assess the approach on regional level across a wide range of climate conditions and on national level. Like several other countries in the Southern Cone, Argentina's cereal crops have been negatively affected by climate change.…”

Section: Introductionmentioning

confidence: 99%

Multi-model ensembles for regional and national wheat yield forecasts in Argentina

Zachow,

Kunstmann,

Miralles

et al. 2024

Environ. Res. Lett.

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While multi-model ensembles (MMEs) of seasonal climate models (SCMs) have been used for crop yield forecasting, there has not been a systematic attempt to select the most skillful SCMs to optimize the performance of a MME and improve in-season yield forecasts. Here, we propose a statistical model to forecast regional and national wheat yield variability from 1993-2016 over the main wheat production area in Argentina. Monthly mean temperature and precipitation from the four months (Aug-Nov) before harvest were used as features. The model was validated for end-of-season estimation in December using reanalysis data (ERA) from the European Centre for Medium-Range Weather Forecasts (ECMWF) as well as for in-season forecasts from June to November using a MME of three SCMs from 10 SCMs analyzed. A benchmark model for end-of-season yield estimation using ERA data achieved a R2 of 0.33, a root-mean-square error (RMSE) of 9.8% and a receiver operating characteristic (ROC) score of 0.8 on national level. On regional level, the model demonstrated the best estimation accuracy in the northern sub-humid Pampas with a R2 of 0.5, a RMSE of 12.6% and a ROC score of 0.9. Across all months of initialization, SCMs from the National Centers for Environmental Prediction, the National Center for Atmospheric Research and the Geophysical Fluid Dynamics Laboratory had the highest mean absolute error of forecasted features compared to ERA data. The most skillful in-season wheat yield forecasts were possible with a 3-member-MME, combining data from the SCMs of the ECMWF, the National Aeronautics and Space Administration and the French national meteorological service. This MME forecasted wheat yield on national level at the beginning of November, one month before harvest, with a R2 of 0.32, a RMSE of 9.9% and a ROC score of 0.7. This approach can be applied to other crops and regions.

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Forecasting soybean production to enhance climate services for Agriculture in Argentina

Cited by 6 publications

References 10 publications

The Effect of Sowing Date on the Growth and Yield of Soybeans Cultivated in North-Eastern Poland

The Effect of Sowing Date on the Growth and Yield of Soybeans Cultivated in North-Eastern Poland

Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere

Multi-model ensembles for regional and national wheat yield forecasts in Argentina

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