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This paper presents the systematic process framework to develop the suitable crop insurance for each agriculture farming region which has individual differences of associated crop, climate condition, including applicable technology to be implemented in crop insurance practice. This paper also studies the adoption of new insurance scheme to assess the willingness to join crop insurance program.

Crop insurance development has been performed through IDDI conceptual framework to illustrate the specific crop insurance diagram. Area-yield insurance as a type of index-based insurance advantages on reducing basis risk, adverse selection and moral hazard. This paper therefore aims to develop area-yield crop insurance, at a provincial level, focusing on rice insurance scheme for the protection of flood. The diagram demonstrates the structure of area-yield rice insurance associates with selected machine learning algorithm to evaluate indemnity payment and premium assessment applicable for Jasmine 105 rice farming in Ubon Ratchathani province. Technology acceptance model (TAM) is used for new insurance adoption testing.

The framework produces the visibly informative structure of crop insurance. Random Forest is the algorithm that gives high accuracy for specific collected data for rice farming in Ubon Ratchathani province to evaluate the rice production to calculate an indemnity payment. TAM shows that the level of adoption is high.

This paper originates the framework to generate the viable crop insurance that suitable to individual farming and contributes the idea of technology implementation in the new service of crop insurance scheme.

The purpose of this paper is to investigate farmers’ risk aversion using the equally likely certainty equivalent approach and the negative exponential utility function to identify risk preference classification.

Stochastic efficiency with respect to a function is applied to determine the risk efficient farming systems for the farmers in central and north-east regions of Thailand.

The study results showed that maize followed by sorghum is the most risk efficient farming system for the extremely risk averse rain-fed farmers in the central region of Thailand. Intensive planting of wet rice and dry rice cultivation is preferred by the extremely risk averse central region irrigated farmers. Wet rice and cassava together with raising small herd of cattle is the most economically viable farming system for the extremely risk averse rain-fed farmers in the north-east region, while two rice crops with raising cattle is preferred by the extremely risk averse north-east irrigated farmers of Thailand.

The findings of this study provide useful information to reinforce the empirical basis for risk analysis for Thai farmers. The results will provide more accurate information regarding risk at the farm level to policy makers and researchers.

This paper seeks to investigate the impact of citrus tristeza virus (CTV) on the commercial value of fruit produced by navel orange trees.

Data are counts of fruit of various sizes and quality harvested from a tree in a year. The counted fruit are converted to a dollar value using a standardized pricing matrix and then normalized as a ratio of the tree value compared to trees in the same orchard and year that were free of virus. Statistical tests determine if trees at various stages of infection have different production values than virus‐free trees.

On average, trees infected with CTV have higher fruit production values than trees that did not contract the virus, after compensating for climate and location differences, even though the presence or absence of CTV explains only about 1 percent of the variation in production value.

Data are from commercially maintained orchards rather than a carefully controlled experiment in an isolated greenhouse environment.

Orange growers in the region should be reluctant to remove trees that have mild strains of CTV. The effects of a tree virus on production value should be a consideration in how to respond to the virus.

Development of a standardized pricing matrix to control for pricing fluctuations from year to year is a relatively novel concept. The applied concepts of tree status cohorts and relative crop values are original and provide valuable tools for combining data from different orchards and climate conditions.

This research examines factors influencing the adoption of crop and revenue insurance. This is accomplished by estimating a multinomial logit model of insurance choices facing U.S. farmers. Results indicate significant differences in the probabilities of adoption of each insurance plan. The levels of selected explanatory variables, such as operator’s education level, debt‐to‐asset ratio, off‐farm income, soil productivity, participation in production and marketing contracts, and type of farm ownership, appear to be the determinants of the probability of having adopted each insurance plan.

As a response to global population growth and increasing demand for food, farmers have been complementing traditional agriculture practices with vertical farming (VF) and indoor hydroponic systems. To facilitate the growth of the VF industry, this paper aims to identify business model elements and their configurations that lead to high firm performance.

The research goals were met by conducting literature reviews coupled with a fuzzy-set qualitative comparative analysis (fsQCA) on five business model elements, “superior” OR “strong” performance as two possible outcomes, and the top-ranked global VF growers listed in the Crunchbase Database.

From the fsQCA results, it was observed that several business model configurations lead to strong firm performance. Vertical farms growing in urban settings and having strong customer engagement platforms, coupled with a presence of business-to-business (B2B) sales channels, are more consistently associated with superior performance. These results imply that the decision configuration of location, along with customer engagement activity and sales activity are differentiating factors between good firm performance and superior firm performance in the case of vertical farms.

This paper contributes to expanding the knowledge of business model theory, business model configurations and VF management, providing specific guidelines for vertical farm owners and investors related to decision-making for higher firm performance, as well as positive environmental, social and economic impact.

The purpose of this paper is to investigate the relationships among choice of risk management tools, perceived effectiveness of risk management tools, self-reported risk attitude, and farm and farmer characteristics.

A mail survey was used to collect information about utilization of risk management tools, perceived effectiveness of risk management tools, and factors that could influence choice of risk management tools by Illinois farmers. Cluster analysis, one-way ANOVA, χ2 tests of independence, and multinomial logistic regression were utilized to detect possible relationships among choice of risk management tools, perceived effectiveness of risk management tools, self-reported risk attitude, and farm and farmer characteristics.

Multinomial logistic regression analysis revealed that age and gross farm income (GFI) were the strongest predictors of the risk management tool utilization group to which an individual would be assigned. The number of risk management tools utilized decreased with age but increased with GFI. Neither self-reported risk attitude nor education was a significant independent variable in the multinomial logistic regression model, but both were strongly impacted by age. Younger farmers with higher GFI were the most likely users of hedging.

The results of this study provide support for the idea that farmers who are better able to generate revenue are better able to manage risk, but the direction of causality was not investigated.

Risk management service providers could benefit from this study as a benchmark for understanding their current and potential farmer clients’ risk management strategies.

This study used cluster analysis and multinomial logistic regression to address the complexity of decisions regarding multiple risk management tools. The number of tools utilized by individuals was investigated.

A common feature of certain kinds of data is a high level of statistical dependence across space and time. This spatial and temporal dependence contains useful information that can be exploited to significantly reduce the uncertainty surrounding local distributions. This chapter develops a methodology for inferring local distributions that incorporates these dependencies. The approach accommodates active learning over space and time, and from aggregate data and distributions to disaggregate individual data and distributions. We combine data sets on Kansas winter wheat yields – annual county-level yields over the period from 1947 through 2000 for all 105 counties in the state of Kansas, and 20,720 individual farm-level sample moments, based on ten years of the reported actual production histories for the winter wheat yields of farmers participating in the United States Department of Agriculture Federal Crop Insurance Corporation Multiple Peril Crop Insurance Program in each of the years 1991–2000. We derive a learning rule that combines statewide, county, and local farm-level data using Bayes’ rule to estimate the moments of individual farm-level crop yield distributions. Information theory and the maximum entropy criterion are used to estimate farm-level crop yield densities from these moments. These posterior densities are found to substantially reduce the bias and volatility of crop insurance premium rates.

The purpose of this paper is to use simulation analysis to assess farmer choice between crop insurance and supplemental revenue options as proposed during development of the Agricultural Act of 2014.

The certainty equivalent of wealth is used to rank farm choices and assess the effects of supplemental revenue options on the crop insurance plan and coverage level chosen by the producer under a range of farm attributes. The risk-reducing effectiveness of the select programs is also examined through their impact on the farm revenue distribution. The dependence structure of yield and prices is modeled by applying copula techniques on historical data.

Farm program supplemental revenue programs generally have no effect on crop insurance choices. Crop insurance supplemental revenue programs typically reduce crop insurance coverage at high coverage levels. An individual plan of crop insurance combined with a supplemental revenue insurance plan may substitute for incumbent area crop insurance plans.

The analysis provides insights into farmers’ possible choices by focussing on alternative crops and farm attributes and extensive scenarios, using current data, crop insurance plans and programs contained in the 2014 Farm Bill and related bills. The results should be of value to policy officials and producers in regards to the design and use of risk management tools.

The purpose of this paper is to identify the trends in crop diversification (CD) while examining its impact on the technical efficiency of peasant farmers in Nigeria.

The paper employs the Herfindahl and Ogive indices to compute the diversification indices and the stochastic frontier production model (SFPM) to estimate the technical efficiency (TE) level of the farms using unbalanced panel data covering three farming seasons (2006/2007 to 2008/2009).

The results of both the Herfindahl and Ogive indices showed that cropping pattern increased significantly with the intensification of crop diversification in the study across the three seasons. The result of the SFPM shows evidence of decreasing returns‐to‐scale and technical progress in the food crop production in the region. Education, extension, and CD are identified as efficiency increasing policy variables while an average TE level of about 81 percent was obtained from the analysis.

To the best of the author's knowledge, this the very first study that employs panel data to analyze technical efficiency of farms in Nigeria.

Hybrid rice is considered as one of the technologies having the potential to push the production frontier to meet the growing demand for rice in India. The technology was introduced in India in 1994 but is yet to see widespread adoption. The purpose of this paper is to identify the factors that influence the partial/complete adoption of hybrid rice technology by the farmers in India. This study also assesses the factors behind difference in the share of land allocated to hybrid rice cultivation by farmers.

The study employs a Tobit model to evaluate the impact of factors related to technology, farmer, farm and geographical location on the decision to adopt hybrid rice. Data for this study are compiled from surveys of 441 hybrid rice growing farmers across 3 Indian states conducted during 2012-2013.

The paper finds that farmers with smaller landholdings, higher education and higher experience of growing hybrid rice are more likely to be complete adopters. Farmers reporting good demand for hybrid rice output and availability of subsidy on hybrid rice seeds also have higher probability of being complete adopters. However, the availability of hybrid rice seeds in government outlets and cultivating multiple kharif crops are negatively related to the extent of hybrid rice adoption. The results suggest insignificant impact of age, family size, ownership of cattle and machinery on the adoption level of hybrid rice by the farmers.

Although the sample for this study has been collected from three states with different agro-climatic zones and productivity, the results cannot be generalized for other states.

There is a great potential to increase the area under hybrid rice cultivation in India. This study is one of the first attempts to look at the adoption levels of hybrid rice in India and determine the factors which might be hindering the complete adoption of the technology. Focusing on the factors positively related to complete adoption can help in enhancing the area under hybrid rice and similar approach can be used for other new agricultural technologies in the developing country context.