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 17 publications

(5 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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“…Farmers can transfer these risks to other parties to overcome losses. One of them is through the paddy agricultural insurance program which is one of the Ministry of Agriculture's programs [4,5]. A lot of research on agricultural insurance has been undertaken, as indicated by various publications that have been published.…”

Section: Introductionmentioning

confidence: 99%

Mapping in the Topic of Mathematical Model in Paddy Agricultural Insurance Based on Bibliometric Analysis: A Systematic Review Approach

2022

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Bibliometric analysis is the quantitative study of bibliographic material. In this paper, a systematic review of papers, authors, and journals is carried out. This is necessary to determine and set targets to be achieved in further research. The main objective of this study is to identify some of the most relevant research and the latest trends according to the information found in the Google Scholar, Publish or Perish, Science Direct, and Dimension databases. The methods used are classification, analysis of the most cited journals of all time, and the most prolific and influential authors. The results are information on the number of papers, citations, researchers, h-index, g-index, major reference journals, and visualization of research roadmaps for the topic of agricultural insurance mathematical models. The research findings identify the core themes, which are used as research gaps for future research.

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

Cited by 17 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

Mapping in the Topic of Mathematical Model in Paddy Agricultural Insurance Based on Bibliometric Analysis: A Systematic Review Approach

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