A multi-model analysis of teleconnected crop  yield variability in a range of cropping systems

Heino, Matias; Guillaume, Joseph H. A.; Müller, Christoph; Iizumi, Toshichika; Kummu, Matti

doi:10.5194/esd-11-113-2020

Cited by 28 publications

(14 citation statements)

References 67 publications

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“…As shown in Figures 2 to 5, many predictors with negative correlations were selected for winter forecasting, while many predictors with positive correlations were utilized for summer. As shown in Figure 2a, the correlations between the precipitation in January from 1970 to 2009, and the predictors revealed the highest value, −0.483 in the EASMI data of the preceding 17 months, i.e., EASMI (17). Other significant correlations were found in TPI ( 7) (−0.420) and HrDL (9) (0.407).…”

Section: Teleconnection Analysismentioning

confidence: 89%

“…As shown in Figure 2(a), the correlations between the precipitation in January from 1970 to 2009, and the predictors revealed the highest value, −0.483 in the EASMI data of the preceding 17 months, i.e., EASMI (17). Other significant correlations were found in TPI ( 7) (−0.420) and HrDL (9) (0.407).…”

Section: Teleconnection Analysismentioning

confidence: 90%

“…The results were collected for all lead months, and it was found that the climate indices with relatively longer lead times exhibited a high application frequency. For precipitation forecasting in January, PNA (16), which is the 16-month preceding data of the PNA index, SOI_EQ (10), and EASMI (17) were applied in many models. For July, CAR (13), WHWP (12), and TNA (12) were frequently applied.…”

Section: Application Frequency Of Climate Indicesmentioning

confidence: 99%

See 2 more Smart Citations

Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models

Kim¹,

Lee²,

Lee³

et al. 2020

Water

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In this study, long-term precipitation forecasting models capable of reflecting constantly changing climate characteristics and providing forecasts for up to 12 months in advance were developed using lagged correlations with global and local climate indices. These models were applied to predict monthly precipitation in the Han River basin, South Korea. Based on the lead month of forecast, 10 climate indices with high correlations were selected and combined to construct four-variable multiple regression models for monthly precipitation forecasting. The forecast results for the analytical period (2010–2019) showed that predictability was low for some summer seasons but satisfactory for other seasons and long periods. In the goodness-of-fit test results, the Nash–Sutcliffe efficiency (0.48–0.57) and the ratio of the root mean square error to the standard deviation of the observation (0.66–0.72) were evaluated to be satisfactory while the percent bias (9.4–15.5%) was evaluated to be between very good and good. Due to the nature of the statistical models, however, the predictability is highly likely to be reduced if climate phenomena that are different from the statistical characteristics of the past appear in the forecast targets or predictors. The forecast results were also presented as tercile probability information (below normal, normal, above normal) through a comparison with the observation data of the past 30 years. The results are expected to be utilized as useful forecast information in practice if the predictability for some periods is improved.

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Section: Teleconnection Analysismentioning

confidence: 89%

Section: Teleconnection Analysismentioning

confidence: 90%

Section: Application Frequency Of Climate Indicesmentioning

confidence: 99%

See 1 more Smart Citation

Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models

Kim¹,

Lee²,

Lee³

et al. 2020

Water

View full text Add to dashboard Cite

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“…Also, the use of combined climate information can improve the understanding of the relationship between crop production and the weather oscillations and improve the resilience of the global food system (food security) to unexpected climate-related shocks [16,[56][57][58].…”

Section: Discussionmentioning

confidence: 99%

Improving Soybean Production Under Light Supplementation at Field - A Case Study

Lemes

Azevedo

Domiciano

et al. 2020

Preprint

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In modern agriculture, there is a growing need for cropping efficiency e low environmental impacts. Diverse technologies are becoming available in a recent wave of modernization and integration of knowledge. The use of high-efficiency light supplementation to plant development is scarce to high-productive crops at field conditions (outdoor). The objectives of this study were to evaluate soybean plant and yield responses in an open commercial area (field scale) cultivated with artificial light supplementation. A commercial irrigated (pivot) area received an illumination system for light supplementation (LS) in the inner pivot spans. The light applied was a composition of blue, green and red bands. The outer pivot spans did not receive light supplementation (nLS). About 40 hours of LS were applied to the plants during the soybean crop cycle. Internode number, plant height, pods per plant were weekly evaluated to compose the area under the progress curve (AUPC). The grain yield was also evaluated at harvest. Analysis of variance and test of averages were used to evaluate the data. The AUPC of the internode number, plant height and pods per plant were 15.6, 23.3 and 25.3% higher than for the LS treatment. The regular soybean cycle (nLS) was about 17 weeks; however, the harvest of the LS treatment happened three weeks later. The grain productivity of the nLS was about 4,500 kg ha-1 (75 bags), and of the LS treatment was about 7,080 kg ha-1 (118 bags) - 57.3% superior. Light supplementation at field scale is a challenge; however, affordable and field resistant technologies are now accessible. The present study is the first report of light supplementation used to improve soybean crop production at field scale. The possibility of using light regulation as an additional technique for increasing yields and sustainable production are also discussed.

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“…Such models can integrate valuable information about physiological processes, sowing time, irrigation blade, fertilizer doses, management of insect pests and plant diseases, and their impacts on the soil-crop-environment relationships (Sihag & Prakash, 2019). Additionally, including climate information in such models can shed light on the relationship between crop production and weather oscillations and which in turn can be used to enhance the resilience of the global food system (food security) to unexpected climate-related shocks (Tamiru & Fekadu, 2019;Mulungu & Ng'Ombe, 2019;Patle et al, 2020;Heino et al, 2020).…”

Section: Journal Of Agricultural Studiesmentioning

confidence: 99%

Improving Soybean Production Using Light Supplementation at Field-Scale: A Case Study

Lemes¹,

Azevedo²,

Domiciano³

et al. 2021

JAS

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In modern agriculture, there is a growing need for increasing crop efficiency while minimizing environmental impacts. The use of high-efficiency light supplementation to enhance plant development is limited for high-productive crops at field conditions (outdoor). This study evaluated the soybean plant’s yield responses in an open commercial area (field scale) cultivated under conditions of artificial light supplementation. A commercial irrigated (pivot) area received an illumination system for light supplementation (LS) in its inner pivot spans. About 40 hours of LS were applied to the plants during the soybean crop cycle. The area’s outer pivot spans did not receive light supplementation (nLS). The internode number, the plant height, the pods per plant were evaluated weekly to compute the area under the progress curve (AUPC). The grain yield at harvest was also assessed. The AUPC of the internode number, plant height and pods per plant were positively affected by the LS treatment. The regular soybean cycle (nLS) is about 17 weeks; however, the LS harvest occurred three weeks later. Light supplementation increased soybean grain yield by 57.3% and profitability by 180% when compared to nLS. Although light supplementation at field scale poses a challenge, it is now affordable since sustainable field resistant technologies are now available. The present study is the first known report of light supplementation used to improve soybean crop production at field scale.

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A multi-model analysis of teleconnected crop yield variability in a range of cropping systems

Cited by 28 publications

References 67 publications

Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models

Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models

Improving Soybean Production Under Light Supplementation at Field - A Case Study

Improving Soybean Production Using Light Supplementation at Field-Scale: A Case Study

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