Performance of Temperature-Related Weather Index for Agricultural Insurance of Three Main Crops in China

Zhang, Jing; Zhang, Zhao; Tao, Fulu

doi:10.1007/s13753-017-0115-z

Cited by 15 publications

(4 citation statements)

References 47 publications

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“…This result was consistent with the conclusion that climate data can achieve much better predictive performance than satellite data (Cai et al 2019) due to its direct impacts on yield. Tao et al (2008) found that major crop yields were significantly related to growing season climate (precipitation, temperature) and Zhang et al (2017) believed that climatic variables are key controls on crop growth and determining yields. The temperature increase during the grain-filling stage can easily shorten the filling time, which affects the formation of yield (Siebert et al 2017;Zampieri et al 2017).…”

Section: The Importance Of Forcing Data Type In Yield Estimationmentioning

confidence: 99%

Towards crop yield estimation at a finer spatial resolution using machine learning methods over agricultural regions

Liao

Niu

et al. 2021

Theor Appl Climatol

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Reliable yield estimation is crucial for food security and agricultural production especially in the intensively agricultural region. This study constructed a gridded yield estimation framework by driving machine learning models with remote sensing vegetation index and meteorological forcing. Among eight machine learning methods, support vector machine (SVM), k-nearest neighbor regression (KNN), and Gaussian process regression (GPR) models outperformed the others. Precipitation, temperature, and the fraction of photosynthetically active radiation are key factors for yield estimation. The yield estimation at county level and regional level were further conducted to explore the scale effect (estimation accuracy varies with spatial resolution). Different scales hold diverse spatial variability information. Finer scales that are more representative of spatial variability generally result in the better accuracy. This study demonstrates that a more accurate yield estimation can be achieved at a finer grid level, thus providing guidelines for agricultural planting structure.

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Section: The Importance Of Forcing Data Type In Yield Estimationmentioning

confidence: 99%

Towards crop yield estimation at a finer spatial resolution using machine learning methods over agricultural regions

Liao

Niu

et al. 2021

Theor Appl Climatol

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“…The average weather conditions that occur with wide area coverage, long time, topography which is influenced by the distance from the sun to the earth. The position of the sun towards the earth also causes climate differences, the area around the equator has a tropical climate, the area between 23.5 o C to 66.5 o C north latitude and south latitude has a subtropical climate, the rest has a polar climate (Riaman et al, 2022b), (Zhang et al, 2017).…”

Section: A Introductionmentioning

confidence: 99%

Mathematical Model of Paddy Production using Cobb Douglas Method Based On Weather Factors

Riaman,

Parmikanti,

Subartiny

et al. 2023

JTAM

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This research was conducted to model paddy production based on weather factors. This needs to be done to predict crop yields and regulate paddy cropping patterns. In setting the cropping pattern, the weather is selected which consists of temperature, wind speed, and rainfall, as a variable factor of production. Meanwhile, other factors (such as fertilization, sunshine, air humidity, etc.) are assumed to be in catteries paribus conditions. The research method used is a mixed method between qualitative methods which are descriptive details and quantitative methods which are based on weather data and Paddy's harvest data. The aim of this research is to analyze the influence of weather on paddy production results. Analysis is done to get the production function. Parameters are estimated using the Ordinary Least Square (OLS) method by minimizing the sum of squared errors. Based on data analysis, a correlation of 0.899 was obtained with a standard error of .051665515. the results of model testing also show significant results with the F statistic obtained at 33.98 with a p-value of 0.028 which is less than 5%. So it can be concluded that there is a significant relationship between weather and paddy productivity. In such a way that the weather can be used as a reference in determining the prediction of loss risk and paddy production. This model can also be recommended for further research, namely to determine insurance losses that may arise when extreme weather events occur.

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“…The other is the system risk of insurance, which implies that risk exposure units are not independent, so the insurance may fail to transfer risks among farmers (Miranda and Glauber 1997;Cummins and Trainar 2009). Based on a simple observable parameter that is highly correlated with losses, weather index insurance (WII) is regarded as an effective tool to mitigate the first problem (Skees et al 1997;Mahul 1999;Martin et al 2001;Vedenov and Barnett 2004; Barnett and Mahul 2007;Zhang et al 2017). The second problem also can be avoided because the precondition of index insurance is that risks of agricultural loss are somewhat spatially correlated (Goodwin 2001;Ibarra and Skees 2007;Barnett et al 2008;Okhrin et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Rainfall-Related Weather Indices for Three Main Crops in China

Zhang

Tao

2020

Int J Disaster Risk Sci

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Rainfall-related hazards-deficit rain and excessive rain-inevitably stress crop production, and weather index insurance is one possible financial tool to mitigate such agro-metrological losses. In this study, we investigated where two rainfall-related weather indicesanomaly-based index (AI) and humidity-based index (HI)-could be best used for three main crops (rice, wheat, and maize) in China's main agricultural zones. A county is defined as an ''insurable county'' if the correlation between a weather index and yield loss was significant. Among maize-cropping counties, both weather indices identified more insurable counties for deficit rain than for excessive rain (AI: 172 vs 63; HI: 182 vs 68); moreover, AI identified lower basis risk for deficit rain in most agricultural zones while HI for excessive rain. For rice, the number of AIinsurable counties was higher than the number of HI-insurable counties for deficit rain (274 vs 164), but lower for excessive rain (199 vs 272); basis risks calculated by two weather indices showed obvious difference only in Zone I. Finally, more wheat-insurable counties (AI: 196 vs 71; HI: 73 vs 59) and smaller basis risk indicate that both weather indices performed better for excessive rain in wheatplanting counties. In addition, most insurable counties showed independent yield loss, but did not necessarily result in effective risk pooling. This study is a primary evaluation of rainfall-related weather indices for the three main crops in China, which will be significantly helpful to the agricultural insurance market and governments' policy making.

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Performance of Temperature-Related Weather Index for Agricultural Insurance of Three Main Crops in China

Cited by 15 publications

References 47 publications

Towards crop yield estimation at a finer spatial resolution using machine learning methods over agricultural regions

Towards crop yield estimation at a finer spatial resolution using machine learning methods over agricultural regions

Mathematical Model of Paddy Production using Cobb Douglas Method Based On Weather Factors

Rainfall-Related Weather Indices for Three Main Crops in China

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