ESTIMATING FARM-LEVEL YIELD DISTRIBUTIONS FOR CORN AND SOYBEANS IN ILLINOIS

Many yield modeling approaches have been developed in attempts to provide accurate characterizations of farm-level yield distributions due to the importance of yield uncertainty in crop insurance design and rating, and for managing farm-level risk. Competing existing models of crop yields accommodate varying skewness, kurtosis, and other departures from normality including features such as multiple modes. Recently, the received view of crop yield modeling has been challenged by Just and Weninger who indicate that there is insufficient evidence to reject normality given data limitations and potential methodological shortcomings in controlling for deterministic components (trend) in yields. They point out that past empirical efforts to estimate and validate specific-farm distributional characterizations have been severely hampered by data limitations. As a result, they argue in favor of normality as an appropriate parameterization of crop yields. This paper investigates alternate representations of farm-level crop yield distributions using a unique data set from the University of Illinois Endowment farms, containing same-site yield observations for a relatively long period of time, and under conditions that closely mirror actual farm conditions in Illinois. Results from alternate econometric model specifications controlling for trend effects suggest that a linear trend provides an adequate representation of crop yields at the farm level during the period covered by the estimations. Specification tests based on a linear-trend model suggest significant heteroskedasticity is present in only a few farms, and that the residuals are white noise. With these data, Jarque-Bera normality test results indicate that normality of detrended yield residuals is rejected by a far greater number of fields than would be explained due to randomness. Thus, to further clarify the issue of yield distribution characterizations, more complete goodness-of-fit measures are compared across a larger set of candidate distributions. The results indicate that the Weibull distribution consistently ranks better than the normal distribution both in fields where normality is rejected and in fields where normality is not rejected. The results highlight the fact that failing to reject normality is not the same as identifying normality as a "best" parameterization, and provide guidance for progressing toward better representations of farm-level crop yields.Productivity Analysis, Research Methods/ Statistical Methods, Teaching/Communication/Extension/Profession,

CROP INSURANCE VALUATION UNDER ALTERNATIVE YIELD DISTRIBUTIONS

Considerable disagreement exists about the most appropriate characterization of farm-level yield distributions. Yet, the economic importance of alternate yield distribution specifications on insurance valuation, product designs and farm-level risk management has not been investigated or documented. The results of this study demonstrate that large differences in expected payments from popular crop insurance products can arise solely from the parameterization chosen to represent yields. The results suggest that the frequently unexamined yield distribution specification may lead to incorrect conclusions in important areas of insurance and risk management research such as policy rating, and assessment of expected payments from policies.Risk and Uncertainty,

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer

Trust within the Organizations of the New Economy: an Empirical Analysis of the Consequences of Institutional Uncertainty

This study investigates the effects of different institutional frameworks on the levels of trust within hierarchies. Following the insight into the changing of labour contracts provided by New Economy theorists and International Labour Organization [ILO] reports, this study investigates the possible differences in the levels of trust between two paradigms: the Old Economy and the New Economy. We argue that singular institutional changes which better characterize the New Economy in the form of environmental uncertainty set considerable constrains on trust development. By approaching trust as a dependent variable in a cross-industrial comparison, a questionnaire survey was carried out in Brazil accessing the levels of trust within seven Brazilian private companies. From the literature review and empirical observation of the reality of these organizations, companies were identified and classified into different groups. The study concludes that relative high institutional uncertainty considerably limits the development of trust levels within those companies operating in the New Economy

Joint identification of contaminant source and aquifer geometry in a sandbox experiment with the restart ensemble Kalman filter

[EN] Contaminant source identification is a key problem in handling groundwater pollution events. The ensemble Kalman filter (EnKF) is used for the spatiotemporal identification of a point contaminant source in a sandbox experiment, together with the identification of the position and length of a vertical plate inserted in the sandbox that modifies the geometry of the system. For the identification of the different parameters, observations in time of solute concentration are used, but not of piezometric head data since they were not available. A restart version of the EnKF is utilized because it is necessary to restart the forecast from time zero after each parameter update. The results show that the restart EnKF is capable of identifying both contaminant source information and aquifer-geometry-related parameters together with an uncertainty estimate of such identification.Financial support to carry out this work was received from the Spanish Ministry of Economy and Competitiveness through project CGL2014-59841-P, and from the Spanish Ministry of Education, Culture and Sports through a fellowship for the mobility of professors in foreign research and higher education institutions to the second author, reference PRX17/00150. The authors also would like to thank Universita degli Studi di Parma for providing the experimental equipment and dataChen, Z.; Gómez-Hernández, JJ.; Xu, T.; Zanini, A. (2018). Joint identification of contaminant source and aquifer geometry in a sandbox experiment with the restart ensemble Kalman filter. Journal of Hydrology. 564:1074-1084. https://doi.org/10.1016/j.jhydrol.2018.07.073S1074108456

Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter

[EN] The joint identification of the parameters defining a contaminant source and the heterogeneous distribution of the hydraulic conductivities of the aquifer where the contamination took place is a difficult task. Previous studies have demonstrated the applicability of the restart normal-score ensemble Kalman filter (rNS-EnKF) in synthetic cases making use of sufficient hydraulic head and concentration data. This study shows an application of the same technique to a non-synthetic case under laboratory conditions and discusses the difficulties found on its application and the avenues taken to solve them. The method is first tested using a synthetic case that mimics the sandbox experiment to establish the minimum number of ensemble members and the best technique to prevent the filter collapsing. The synthetic case shows that among different techniques based on update damping and covariance inflation, the Bauser's covariance inflation method works best in preventing filter collapse. Its application to the sandbox data shows that the rNS-EnKF can benefit from Bauser's inflation to reduce the number of ensemble realizations substantially in comparison with a filter without inflation, arriving at a good joint identification of both the contaminant source and the spatial heterogeneity of the conductivities.Financial support to carry out this work was received from the Spanish Ministry of Science and Innovation through project PID2019-109131RB-I00, and from the Spanish Ministry of Education through project PRX17/00150. Teng Xu also acknowledges the financial support from the Fundamental Research Funds for the Central Universities (B200201015) and Jiangsu Specially-Appointed Professor Program (B19052). And the authors would like to thank University of Parma for providing the experimental equipment. Part of the work was performed during a stay of the third author at the University of Parma under the TeachinParma initiative, co-funded by Fondazione Cariparma and University of Parma.Chen, Z.; Xu, T.; Gómez-Hernández, JJ.; Zanini, A. (2021). Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter. Mathematical Geosciences. 53(7):1587-1615. https://doi.org/10.1007/s11004-021-09928-yS1587161553