An evaluation of the Standardized Precipitation Index for assessing inter‐annual rice yield variability in the Ganges–Brahmaputra–Meghna region

Kattelus, Mirja; Salmivaara, Aura; Mellin, Ilkka; Varis, Olli; Kummu, Matti

doi:10.1002/joc.4489

Cited by 17 publications

(16 citation statements)

References 53 publications

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“…This index has many advantages such as simple calculation, good stability, multiple time scales, and space–time comparability. It has been widely used in drought monitoring 43,44 and can be used to accurately reflect the meteorological drought characteristics in Northwest China 45–48 . SPI assumes that changes in precipitation follow a gamma distribution and it uses mathematical methods to convert the cumulative frequency distribution of precipitation into a standard normal distribution.…”

Section: Methodsmentioning

confidence: 99%

Characteristics of drought vulnerability for maize in the eastern part of Northwest China

Wang

Zhao

Zhang

et al. 2019

Sci Rep

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Based on information distribution and diffusion method theory and combined with the standardized precipitation index and relative meteorological yield data, meteorological factors and social factors were comprehensively considered to assess the vulnerability of maize (Zea mays) to drought. The probability distribution curve of meteorological drought degree (MDD) and relative meteorological yield in the eastern part of Northwest China (Gansu, Ningxia and Shaanxi) from 1978 to 2016 were obtained, using a two-dimensional normal information diffusion method to construct the vulnerability relationship between MDD and relative meteorological yield. The drought vulnerability curve of maize in the study area was obtained. The probability distribution of MDD was multiplied by the fragility curve and summed to obtain the multi-year average risk. The MDD probability distribution curve showed that the probability of moderate drought in Shaanxi was relatively high, followed by Gansu and Ningxia. The probability distribution of Gansu was more discrete. The probability of strong meteorological drought in Ningxia was high, followed by Shaanxi and Gansu. Probability distribution of relative meteorological yield for maize in Gansu Province was highly discrete, with thick tailings, large uncertainties, and more extreme values, which were strongly affected by meteorological conditions, followed by Shaanxi and Ningxia. Taking meteorological drought as the cause and maize damage as the result, the vulnerability relationship between MDD and drought damage was obtained. With an increased MDD, the relative meteorological yield of maize gradually declined. From the average value, when MDD was less than −2.60, the relative meteorological yield of maize was reduced within 15%; when MDD was greater than −2.60, the relative meteorological yield of maize increased within 10%. When the degree of meteorological drought exceeded −2.2, maize was most vulnerable to drought in Shaanxi followed by Ningxia and Gansu. When meteorological drought was less than −2.2, maize was most vulnerable to drought in Shaanxi followed by Gansu and Ningxia. The expected values of relative meteorological production in Gansu, Ningxia, and Shaanxi were 1.36%, 2.48%, and −1.76%, respectively; therefore, Shaanxi had the highest maize drought risk, followed by Gansu and Ningxia. This research had a clear physical background and clear risk connotations. The results provide a data foundation and a theoretical basis for drought disaster reduction for maize in the study area.

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Section: Methodsmentioning

confidence: 99%

Characteristics of drought vulnerability for maize in the eastern part of Northwest China

Wang

Zhao

Zhang

et al. 2019

Sci Rep

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“…In this study, when spring wheat yields were regressed against single water supply factor, precipitation during spring wheat growing season and soil water content in 50 cm depth, respectively, we found that the single factor could only explain as high as 30 variation of yield. However, in some studies, precipitation during growing season had significant relationship with crop yield Parthasarathy et al, 1988;Sneva, 1982 , and drought indices based on precipitation and other climatic factors during the crop growing period could precisely monitor agricultural drought Kattelus et al, 2016;Potopová et al, 2016;Yamoah et al, 2000 . The cause of the different results is that some crops have longer growing season, whereas others have shorter growing season.…”

Section: Discussionmentioning

confidence: 99%

Determining agricultural drought for spring wheat with statistical models in a semi-arid climate

Zhao

Wang

et al. 2018

J. Agric. Meteorol.

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Agricultural drought frequently occurs and results in major grain yield loss in semi-arid climate region, but determining it is difficult. This study was conducted to determine agricultural drought for spring wheat Triticum aestivum L. in the western Loess Plateau of China. Several statistical models were established and evaluated by long-term data, including soil water in soil layer of 50 cm depth at sowing day, air temperature, precipitation, pan evaporation during spring wheat growing season, and two groups of spring wheat yield one from field experiments during 1987-2011 and the other from statistical Bureau during 1980-2013. Even though each of water supply factors, precipitation during growing season and the soil water at sowing day, could separately explain no more than 30 variation of the yield, both of them could explain > 55 yield variation under dry condition. Average air temperature and precipitation during growing season that displayed two apparent yield categories drought and normal could be used to determine agricultural drought by pattern recognition when years with the soil water at sowing day of > 98.4 mm were eliminated. Based on long-term meteorological data and the relationship between soil water at sowing day and yield under different growing season moisture conditions, the probability of agricultural drought occurrence in Dingxi for spring wheat was speculated, which nearly corresponds with the observational data during 1980-2013.

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“…For instance,calculation of 4-month timescale for April (named SPI-4-4) based on summation precipitation for January, February, March, and April. Computation of SPI using software of US National Drought Mitigation Center (US-NDMC) available at https://drought.unl.edu/droughtmonitoring/SPI/SPIProgram.aspx [15], [16].…”

Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

“…Rice yield changes in time depend on several factors, such as weather factors, new management practices and technologies [15]. To capture the effect of weather factors, a linear time trend was removed from yield data and the proportional yield deviation (YD) calculated as follows:…”

Section: 4rice Yield (Qha -1 )mentioning

confidence: 99%

Comparing Decision Tree, Random Forest and Boosting in Identifying Weather Index for Rice Yield Prediction

Masjkur¹,

Tan²

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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Modeling relationship of weather index and yield losses is a basis for developing weather-based index crop insurance. The data mining approach may overcome some limitations of traditional regression approaches to identify a weather index for predicting crop yield. The purpose of study is to evaluate performance Decision Tree, Random Forest and Boosting in identifying most important weather index for rice crop yield prediction. The study using district level of rice yield data of 8 locations within the annually period of 1991-2014 in Java region. The corresponding weather data consist of 48 weather variables including timescale Standardized Precipitation Index (SPI), Growing Degree Days (GDD), and Vapor Pressure Deficit (VPD) for growing season, respectively. Results show that Boosted Regression Tree is the best model compared to Regression Tree and Random Forest for rice yield prediction. The most important weather index is Growing Degree Days on growing season I (GDD I) and Growing Degree Days on growing season III (GDD III).The threshold values of GDD I > 2100 0 C and GDD III > 2150 0 C would trigger rice yield losses.

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An evaluation of the Standardized Precipitation Index for assessing inter‐annual rice yield variability in the Ganges–Brahmaputra–Meghna region

Cited by 17 publications

References 53 publications

Characteristics of drought vulnerability for maize in the eastern part of Northwest China

Characteristics of drought vulnerability for maize in the eastern part of Northwest China

Determining agricultural drought for spring wheat with statistical models in a semi-arid climate

Comparing Decision Tree, Random Forest and Boosting in Identifying Weather Index for Rice Yield Prediction

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