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The purpose of this paper is to propose going one step further in the simulation tools related to agriculture by integrating fleets of mobile robots for the execution of precision agriculture techniques. The proposed new simulation environment allows the user to define different mobiles robots and agricultural implements.

With this computational tool, the crop field, the fleet of robots and the different sensors and actuators that are incorporated into each robot can be configured by means of two interfaces: a configuration interface and a graphical interface, which interact with each other.

The system presented in this article unifies two very different areas – robotics and agriculture – to study and evaluate the implementation of precision agriculture techniques in a 3D virtual world. The simulation environment allows the users to represent realistic characteristics from a defined location and to model different variabilities that may affect the task performance accuracy of the fleet of robots.

This simulation environment, the first in incorporating fleets of heterogeneous mobile robots, provides realistic 3D simulations and videos, which grant a good representation and a better understanding of the robot labor in agricultural activities for researchers and engineers from different areas, who could be involved in the design and application of precision agriculture techniques. The environment is available at the internet, which is an added value for its expansion in the agriculture/robotics family.

The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.

The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.

Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.

Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.

Describes a simple framework for examining the factors affecting crop production, and explains how this framework can be extended to more complex simulation models of crop growth. Considers the uses and current limitations of such simulation models in predicting the effect of climate change on crop growth, and suggests how the models could be used to assess the impact of climate change and set the levels of emissions for management.

The purpose of this study is to simulate the climate change impacts on winter wheat production and evaluate the possibilities of using various varieties and shifting planting date as two climate change adaptation strategies in Kerman Province, Iran.

Two types of global circulation model and three scenarios for three periods were used. Daily climatic parameters were generated by LARS-WG (Long Ashton Research Station-Weather Generator). The CERES-wheat model was used to simulate future winter wheat growth, development and production.

The results showed that CO₂ had no effect on the phenology of winter wheat, and the negative impact of temperature on the grain yield was higher than the positive effect of CO₂ enrichment. The length of the reproductive growth period of the winter wheat was significantly shortened as affected by the negative impacts of rise in temperature. The simulated results indicated that the grain yield of common (medium maturing) variety of winter wheat will decline, ranging from −0.27 to −18.71 per cent according to future climate changes. Adaptation strategies showed that the early maturing variety had a higher and more stable grain yield under climate change conditions than medium and delayed maturing varieties. Earlier planting date (20 October) increased wheat grain yield under future climatic conditions than common (November 5) planting date. In reverse, later planting (November 20) would accelerate harmful effects of climate change on wheat grain yield.

The results highlighted the potential of early maturing variety and early planting date as the appropriate agronomical approaches for mitigating harmful impacts of climate change on winter wheat production in arid regions.

The empirical literature on climate change and agriculture does not adequately address the issue of potential endogeneity between climatic variables and agriculture, which makes their estimates unreliable. This paper aims to investigate the relationships between climate change and agriculture and test the potential reverse causality and endogeneity of climatic variables to agriculture.

This study introduces a geographical instrument, longitude and latitude, for temperature to assess the impact of climate change on agriculture by estimating regression using IV-two-stage least squares method over annual panel data for 60 countries for the period of 1999-2011. The identification and F-statistic tests are used to choose and exclude the instrument. The inclusion of some control variables is supposed to reduce the omitted variable bias.

The study finds a negative relationship between temperature and agriculture. Surprisingly, the magnitude of the coefficient on temperature is mild, at least 20 per cent, as compared to previous studies, which may be because of the use of the instrumental variable (IV), which is also supported by an alternative robust measure when estimated across different regions.

The study provides strong implications for policymakers to confront climate change, which is an impending danger to agriculture. In designing effective policies and strategies, policymakers should focus not only on crop production but also on other agricultural activities such as livestock production and fisheries, in addition to national and international socio-economic and geopolitical dynamics.

This paper contributes to the growing literature in at least four aspects. First, empirical settings introduce an innovative geographical instrument, Second, it includes a wider set of control variables in the analysis. Third, it extends previous studies by involving agriculture value addition. Finally, the effects of temperature and precipitation on a single aggregate measure, agriculture value addition, are separately investigated.

Purpose – The chapter examines the international welfare effects of biotech crop adoption, based on a transversal literature review and a case study of the introduction of genetically modified (GM) food crops in Bangladesh, India, Indonesia, and the Philippines.

Methodology/approach – The analysis is based on (a) a review of lessons from the applied economic literature and (b) simulations using an improved multimarket, multicountry, computable general equilibrium (CGE) model, calibrated with productivity hypotheses formulated with local scientists in the four Asian countries.

Findings – Results from the analysis show that, in the absence of trade-related regulations, GM crop adoption generates economic gains for adopting countries and importing non-adopters, that domestic regulations at adopters and especially non-adopters can reduce these gains, and that import regulations in other countries can also affect gains for exporting adopters. The case study illustrates these conclusions, but it also shows that net importers will mostly benefit from adoption in their terms of trade, and that segregation of non-GM crops for export markets can be beneficial if it is not too costly.

Research limitations/implications – The use of a CGE model allows for accounting for cross-sectoral effects, and for regulations affecting bilateral trade flows, but it also has a number of limitations. The model used here, like the ones used in the other papers in the literature, is static, based on an aggregated representation of the global economy (GTAP database), and assumes perfect competition. This means that the absolute results of each scenario may not perfectly represent the actual welfare effects engendered by the adoption of biotech crops. Still, what matters here is the comparison of the relative welfare effects across countries and scenarios. The simulations are also done ex-ante, so, even if the model here was calibrated with country-based data, the results do depend on hypothetical assumptions about the performance of the selected technologies.

Originality/value of the paper – The chapter aims to illustrate the welfare effects generated by GM crops for adopters, non-adopters, in a segmented and regulated international market. Unlike other papers, the review section provides key transversal lessons from the literature, accounting for results from both partial equilibrium and CGE model studies. The empirical application focuses on four populous Asian countries that have been largely left out of the literature. The model used in the simulation presents a number of improvement from the CGE literature on GM crops, including partial adoption, factor-biased productivity shock in each adopting country, GM labeling regulations modeled as trade filters, and the inclusion of costly non-GM segregation as observed in the international market.

A simulation methodology is applied to the loan loss reserve process of an agricultural lender. Weaknesses of the point‐estimate approach to estimating loan loss reserves are addressed with a “bottom‐up” model. Modeling includes consideration of the producer’s and the lender’s diversification efforts. Implementation of this model will provide the lender a better understanding of the institution’s portfolio risk, as well as the credit risk associated with each loan. This study compares the lender’s loan loss estimates to a distribution of losses with associated probabilities. The comparative results could provide the lender a basis for setting probability levels for determining the regulatory required level of loan loss reserve.

The authors aim to provide here an opinion on the state‐of‐the‐art of integrated ecological‐economic assessments of bioenergy under climate change, as well as the challenges along with their implications faced in planning adaptation at local scale.

Investments to reduce emissions must be made in the coming decades to avoid the risks posed by climate change. If these investments are made wisely, then costs will be manageable, stability in markets as well as energy security will be achieved, and even rural development and economic growth may be stimulated. The authors call attention to the need for modeling of climate change impacts by combining the outputs from appropriately designed crop simulation models with economic analyses. Combining natural science and economics in a compatible fashion at local scale will play an essential role in advancing communication and information exchange.

There are key differences in drivers or determinants of mitigation and adaptation potential and decisions at different scales, which means that different actors, different timescales and different spatial scales of decision making must be specifically considered. Understanding of the potential impacts of climate change requires disaggregation of the agricultural sector with appropriate detail. A critical trade‐off exists between area‐wide spatial coverage and an explicit consideration of local peculiarities.

The authors suggest that a much stronger effort must be made to meld natural science crop modeling approaches with economic analyses, to include spatially explicit consideration of conventional crop production along with 1st and 2nd generation bioenergy crops, and the evaluation not only of “best guess” scenarios of change, but also potential system impacts of extreme scenarios.

Understanding farmers’ preferences for crop insurance attributes is crucial in designing better insurance products and guiding government policies but such research is lacking, particularly in developing countries. The paper aims to discuss these issues.

This study uses a survey featuring a discrete choice experiment and policy simulation.

Overall, crop insurance has positive values to farmers, although preference is heterogeneous based on socioeconomic characteristics and risk position. Policy simulation confirms the roles of liability in strengthening insurance participants’ welfare and premium subsidy in encouraging participation. Introducing one more product into the market can accommodate farmers’ diverse needs and lead to increases in both aggregated social welfare and participation while maintaining the current level of government expense in subsidy – a potential Pareto improvement.

Methodology employed is not the most novel in the choice experiment literature as many of the advances in choice experiment design could not be applied due to the actual condition in rural China and Chinese farmers’ capability in understanding the experiment.

The results indicate that the current single-product market structure using “low liability with high premium subsidies” cannot accommodate the diverse needs among farmers. Providing more varieties of liability-subsidy combinations, e.g. a high liability with low premium subsidy insurance product, can substantially improve participants’ welfare with little impact to the probability of participation.

The authors believe that this is one of the very few studies that that analyze farmers’ preferences and willingness to pay for the attributes of crop insurance products. It also shows how crop insurance product design can build upon farmers’ choices to achieve a potential Pareto improvement in aggregated social welfare in the context of a fast-developing crop insurance market.

The purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a scientific and reasonable system of agrometeorological disasters prevention and reduction and guaranteeing grain security.

Firstly, according to the statistical data of areas covered by natural disaster, areas affected by natural disaster, sown area of grain crops and output of grain crops from 1979 to 2018 in Henan Province, China. We have constructed an agrometeorological disaster risk assessment system for Henan province, China, which is composed of indicators such as rate covered by natural disaster, rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability. The variation characteristics of agrometeorological disasters in Henan Province and their effects on agricultural production are analyzed. Secondly, the grey relational analysis method is used to analyze the relation degree between the main agrometeorological disaster factors and the output of grain crops of Henan Province. Based on the grey BP neural network, the rate covered by various natural disaster and the rate affected by various natural disaster are simulated and predicted.

The results show that: (1) the freeze injury in the study period has a greater contingency, the intensity of the disaster is also greater, followed by floods. Droughts, windstorm and hail are Henan Province normal disasters. (2) According to the degree of disaster vulnerability, the ability to resist agricultural disasters in Henan Province is weak. (3) During the study period, drought and flood are the key agrometeorological disasters affecting the grain output of Henan Province, China.

The systematic analysis and evaluation of agrometeorological disasters are conducive to the sustainable development of agriculture, and at the same time, it can provide appropriate and effective measures for the assessment and reduction of economic losses and risks.

By calculating and analyzing the rate covered by natural disaster, the rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability of crops in Henan Province of China and using grey BP neural network simulation projections for the rate covered by various natural disaster and the rate affected by various natural disaster, the risk assessment system of agrometeorological disasters in Henan is constructed, which provides a scientific basis for systematic analysis and evaluation of agrometeorological disasters.