scholarly journals Investigating the performance of different estimation techniques for crop yield data analysis in crop insurance applications

2013 ◽  
Vol 44 (2) ◽  
pp. 217-230 ◽  
Author(s):  
Robert Finger
Keyword(s):  
Data Analysis ◽  
Crop Yield ◽  
Crop Insurance ◽  
Yield Data ◽  
Estimation Techniques

Improving the spatial resolution and ecostratification of crop yield estimates in Canada

2015 ◽  
Vol 95 (3) ◽  
pp. 287-297 ◽  
Author(s):  
Yuneng Du ◽  
Ted Huffman ◽  
Bahram Daneshfar ◽  
Melodie Green ◽  
Feng Feng ◽  
...  
Keyword(s):  
Land Use ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Crop Insurance ◽  
Climatic Conditions ◽  
National Data ◽  
Yield Data ◽  
High Level ◽  
Insurance Data ◽  
Broad Level

Du, Y., Huffman, T., Daneshfar, B., Green, M., Feng, F., Liu, J., Liu, T. and Liu, H. 2015. Improving the spatial resolution and ecostratification of crop yield estimates in Canada. Can. J. Soil Sci. 95: 287–297. Canada's terrestrial ecostratification framework provides nested spatial units for organizing national data related to soils, landforms and land use. In the agricultural domain, the lack of national, uniform crop yield data on the ecostratification framework severely hinders our ability to evaluate the biophysical data with respect to economic and climatic conditions. We developed a national crop yield database at the regional (ecodistrict) level by aggregating individual records of an existing but very broad-level sample-derived yield database according to the ecostratification hierarchy. Issues related to the different sampling frameworks and the need for confidentiality of individual records were resolved in order to generate an ecostratified crop yield dataset at a reasonably detailed spatial scale. Sixty crops were first statistically arranged into 37 agronomically similar crop groups in order to increase class size, and these crop groups were aggregated into increasingly large spatial units until confidentiality was assured. The methodology maintained data quality and confidentiality while producing crop yield estimates at the ecodistrict level. Comparison to independent crop insurance data confirmed that the resulting crop yield data are valid where estimates were derived from data released at the level of an ecodistrict or an ecoregion, but not at the ecoprovince level. Our crop yield estimates offer a reasonably high level of spatial precision while remaining within standard confidentiality constraints.

How important is the transitional yield (t-yield)? An analysis of reforms to organic crop insurance

2019 ◽  
Vol 79 (2) ◽  
pp. 234-254 ◽  
Author(s):  
Timothy A. Delbridge ◽  
Robert P. King
Keyword(s):  
Crop Yield ◽  
Crop Insurance ◽  
Yield Potential ◽  
Yield Data ◽  
Data Set ◽  
Content Type ◽  
Insurance Program ◽  
Before And After ◽  
Production History ◽  
The Impact

PurposeThe USDA’s Risk Management Agency (RMA) made several changes to the crop insurance products available to organic growers for the 2014 crop year. Most notably, a 5 percent premium surcharge was removed and organic-specific transitional yields (t-yields) were issued for the first time. The purpose of this paper is to use farm-level organic crop yield data to analyze the impact of these reforms on producer insurance outcomes and compare the insurance options for new organic growers.Design/methodology/approachThis study uses a unique panel data set of organic corn and soybean yields to analyze the impact of organic crop insurance reforms. Actual Production History values and premium rates are calculated for each farm and crop yield sequence. Producer loss ratios and subsidized premium wedges are compared for yield, revenue and area-risk products before and after the instituted reforms.FindingsResults indicate that RMA succeeded in improving the actuarial soundness of the organic insurance program, though further refinement of organic t-yields may be necessary to accurately reflect the yield potential of organic producers and avoid reductions in program participation.Originality/valueThis paper provides insight into the effectiveness of reforms intended to improve the actuarial soundness of organic crop insurance and demonstrates the effect that the reforms are likely to have on new and existing organic farms. Because this analysis uses data collected independently of RMA and includes farms that may or may not have purchased crop insurance, it avoids the self-selection problems that might affect analyses using crop insurance program data.

scholarly journals The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

2021 ◽  
Vol 13 (4) ◽  
pp. 2362
Author(s):  
Thomas M. Koutsos ◽  
Georgios C. Menexes ◽  
Andreas P. Mamolos
Keyword(s):  
Decision Making ◽  
Spatial Analysis ◽  
Crop Yield ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Yield Data ◽  
Site Specific ◽  
Production Areas ◽  
Short Time ◽  
Monitor Data

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.

Diversifying systemic risk in agriculture

2016 ◽  
Vol 76 (4) ◽  
pp. 512-531 ◽  
Author(s):  
Xiaoguang Feng ◽  
Dermot Hayes
Keyword(s):  
Crop Yield ◽  
Systemic Risk ◽  
Crop Insurance ◽  
Tail Dependence ◽  
Model Potential ◽  
High Dimensional ◽  
Computationally Efficient ◽  
Content Type ◽  
Insurance Portfolio ◽  
Systemic Nature

Purpose Portfolio risk in crop insurance due to the systemic nature of crop yield losses has inhibited the development of private crop insurance markets. Government subsidy or reinsurance has therefore been used to support crop insurance programs. The purpose of this paper is to investigate the possibility of converting systemic crop yield risk into “poolable” risk. Specifically, this study examines whether it is possible to remove the co-movement as well as tail dependence of crop yield variables by enlarging the risk pool across different crops and countries. Design/methodology/approach Hierarchical Kendall copula (HKC) models are used to model potential non-linear correlations of the high-dimensional crop yield variables. A Bayesian estimation approach is applied to account for estimation risk in the copula parameters. A synthetic insurance portfolio is used to evaluate the systemic risk and diversification effect. Findings The results indicate that the systemic nature – both positive correlation and lower tail dependence – of crop yield risks can be eliminated by combining crop insurance policies across crops and countries. Originality/value The study applies the HKC in the context of agricultural risks. Compared to other advanced copulas, the HKC achieves both flexibility and parsimony. The flexibility of the HKC makes it appropriate to precisely represent various correlation structures of crop yield risks while the parsimony makes it computationally efficient in modeling high-dimensional correlation structure.

scholarly journals Geostatistical analysis of crop yield maps in a long term no tillage system

Bragantia ◽  
2010 ◽  
Vol 69 (suppl) ◽  
pp. 9-18 ◽  
Author(s):  
Osvaldo Guedes Filho ◽  
Sidney Rosa Vieira ◽  
Marcio Koiti Chiba ◽  
Célia Regina Grego
Keyword(s):  
Crop Yield ◽  
Crop Yields ◽  
Spherical Model ◽  
Exponential Model ◽  
Spatial And Temporal Variability ◽  
No Tillage ◽  
Yield Data ◽  
The North ◽  
Tillage System

It is known, for a long time, that crop yields are not uniform at the field. In some places, it is possible to distinguish sites with both low and high yields even within the same area. This work aimed to evaluate the spatial and temporal variability of some crop yields and to identify potential zones for site specific management in an area under no-tillage system for 23 years. Data were analyzed from a 3.42 ha long term experimental area at the Centro Experimental Central of the Instituto Agronômico, located in Campinas, Sao Paulo State, Brazil. The crop yield data evaluated included the following crops: soybean, maize, lablab and triticale, and all of them were cultivated since 1985 and sampled at a regular grid of 302 points. Data were normalized and analyzed using descriptive statistics and geostatistical tools in order to demonstrate and describe the structure of the spatial variability. All crop yields showed high variability. All of them also showed spatial dependence and were fitted to the spherical model, except for the yield of the maize in 1999 productivity which was fitted to the exponential model. The north part of the area presented repeated high values of productivity in some years. There was a positive cross correlation amongst the productivity values, especially for the maize crops.

Factors Influencing Selection of E-Business Among SME Entrepreneurs in Malaysia

2021 ◽  
pp. 289-298
Author(s):  
S. M. Ferdous Azam ◽  
Jacquline Tham ◽  
Ali Khatibi
Keyword(s):  
Data Analysis ◽  
Homogeneity Tests ◽  
Chi Square ◽  
Estimation Techniques ◽  
Factors Influencing ◽  
Present Understanding ◽  
Selection Of

This chapter aims to establish the present understanding and commonality with the variables that affect e-business allocation. The model was tried with 200 Malaysian respondents as an example. The questionnaires were distributed to SME business visionaries in a variety of sectors. Additionally, a role connection for the extent of exposure to e-business appropriation was constructed. Data analysis was undertaken using various perspectives, ordinary tests, homogeneity tests, relationship tests, ANOVA, multicollinearity, and study estimation techniques, including chi-square. The findings indicated that each of the quantifiable variables had an effect on SMEs entrepreneurs' decision to engage in e-commerce.

Forecasting the Crop Yield Production in Trichy District Using Fuzzy C-Means Algorithm and Multilayer Perceptron (MLP)

2020 ◽  
Vol 11 (3) ◽  
pp. 83-98
Author(s):  
Geetha M. C. S. ◽  
Elizabeth Shanthi I.
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Multilayer Perceptron ◽  
Training Data ◽  
Yield Data ◽  
Economic Determinants ◽  
Fuzzy C Means ◽  
Yield Production ◽  
Yield Forecast ◽  
Fuzzy C Means Algorithm

The agricultural stock depends upon several factors like biological, seasonal, and economic determinants. The growers sustain a vital loss if they are not capable of predicting the variations in these circumstances. The uncertainty on crop yield can be predicted in a logical and mathematical way. The forecast is made based on the previous archives of yield data secured from that area. Data mining is one such procedure practised to predict the crop yield. The systems examine the data, and on mining, several patterns based on numerous parameters predict the return. This article directs on crop yield forecast in Trichy district by adopting data mining techniques for rule formation on classifying the training data and implementing prediction for test data. The suggested method employs fuzzy C means algorithm for clustering and multilayer perceptron design for prediction. The results of accuracy and execution time of the proposed system correlated with the regression algorithm of prediction.

Perceptual Mapping Based on Idiosyncratic Sets of Attributes

1994 ◽  
Vol 31 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Jan-Benedict E.M. Steenkamp ◽  
Hans C.M. Van Trijp ◽  
Jos M.F. Ten Berge
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Predictive Validity ◽  
Structural Reliability ◽  
Procrustes Analysis ◽  
Mapping Procedure ◽  
Estimation Techniques ◽  
Perceptual Mapping ◽  
Generalized Procrustes Analysis ◽  
Mapping Techniques

The authors describe a compositional perceptual mapping procedure, unrestricted attribute-elicitation mapping (UAM), which allows consumers to describe and rate the brands in their own terminology and thus relaxes the restrictive assumptions of traditional compositional mapping techniques regarding the structure and interpretation of the set of attributes. They compare the performance of three estimation techniques for constructing a group space based on the idiosyncratic data, namely, INDSCAL, CANCOR, and generalized procrustes analysis (GPA). Their findings indicate that the three estimation techniques perform about equally well. UAM also is compared with traditional compositional mapping. They find that UAM is superior on fit of the solution, interpretability, and sample reliability. UAM probably holds an edge with respect to data collection. Traditional compositional mapping is superior on ease of data analysis. No major difference was found on predictive validity and structural reliability.

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