Patents and Haploid Plants

One of the important themes in any discussion concerning the application of haploids in agricultural biotechnology or elsewhere is the role of Intellectual Property Rights (IPR). This term covers both the content of patents and the confidential expertise, usually related to methodology and referred to as "Trade Secrets". This review will explain the concepts behind patent protection, and will use the international patent databases to analyse the content of these patents and trends over the last 20 years. This analysis from regions including North America, Europe, and Asia reveals a total of more than 30 granted patents and a larger number of applications. The first of these patents dates from 1986, and although the peak of activity was in the late 1990s, there has been continuous interest to the present day. The subject matter of these patents and applications covers methods for anther and pollen culture, ovule culture, the use of specific haploid-inducing genes, the use of haploids as transformation targets, and the exploitation of genes that regulate embryo development. The species mentioned include cereals, vegetables, flowers, spices and trees.

Monthly average temperature modelling

This research is associated with the goal of the horticultural sector of the Colombian southwest, which is to obtain climatic information, specifically, to predict the monthly average temperature in sites where it has not been measured. The data correspond to monthly average temperature, and were recorded in meteorological stations at Valle del Cauca, Colombia, South America. Two components are identified in the data of this research: (1) a component due to the temporal aspects, determined by characteristics of the time series, distribution of the monthly average temperature through the months and the temporal phenomena, which increased (El Nino) and decreased (La Nina) the temperature values, and (2) a component due to the sites, which is determined for the clear differentiation of two populations, the valley and the mountains, which are associated with the pattern of monthly average temperature and with the altitude. Finally, due to the closeness between meteorological stations it is possible to find spatial correlation between data from nearby sites. In the first instance a random coefficient model without spatial covariance structure in the errors is obtained by month and geographical location (mountains and valley, respectively). Models for wet periods in mountains show a normal distribution in the errors; models for the valley and dry periods in mountains do not exhibit a normal pattern in the errors. In models of mountains and wet periods, omni-directional weighted variograms for residuals show spatial continuity. The random coefficient model without spatial covariance structure in the errors and the random coefficient model with spatial covariance structure in the errors are capturing the influence of the El Nino and La Nina phenomena, which indicates that the inclusion of the random part in the model is appropriate. The altitude variable contributes significantly in the models for mountains. In general, the cross-validation process indicates that the random coefficient model with spatial spherical and the random coefficient model with spatial Gaussian are the best models for the wet periods in mountains, and the worst model is the model used by the Colombian Institute for Meteorology, Hydrology and Environmental Studies (IDEAM) to predict temperature.