Computational modeling for irrigated agriculture planning. Part II: risk analysis

Techniques of evaluation of risks coming from inherent uncertainties to the agricultural activity should accompany planning studies. The risk analysis should be carried out by risk simulation using techniques as the Monte Carlo method. This study was carried out to develop a computer program so-called P-RISCO for the application of risky simulations on linear programming models, to apply to a case study, as well to test the results comparatively to the @RISK program. In the risk analysis it was observed that the average of the output variable total net present value, U, was considerably lower than the maximum U value obtained from the linear programming model. It was also verified that the enterprise will be front to expressive risk of shortage of water in the month of April, what doesn't happen for the cropping pattern obtained by the minimization of the irrigation requirement in the months of April in the four years. The scenario analysis indicated that the sale price of the passion fruit crop exercises expressive influence on the financial performance of the enterprise. In the comparative analysis it was verified the equivalence of P-RISCO and @RISK programs in the execution of the risk simulation for the considered scenario.

Applicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil

Intensification of agricultural production without a sound management and regulations can lead to severe environmental problems, as in Western Santa Catarina State, Brazil, where intensive swine production has caused large accumulations of manure and consequently water pollution. Natural resource scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world, quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho River, near the city of Seara, in Santa Catarina State. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated based on a best guess for model parameters and on a pragmatic sensitivity analysis. The parameters were adjusted to match model outputs (runoff volume, peak runoff rate and sediment concentration) closely with the sparse observed data. A modelling grid cell resolution of 150 m adduced appropriate and computer-fit results. The rainfall runoff response of the AgNPS model was calibrated using three separate rainfall ranges (< 25, 25-60, > 60 mm). Predicted sediment concentrations were consistently six to ten times higher than observed, probably due to sediment trapping along vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in the literature, was able to compensate in part for limited calibration data. Several scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) could be compared by the model, using a relative ranking rather than quantitative predictions.

DNA multigene sequencing of topotypic specimens of the fascioliasis vector Lymnaea diaphana and phylogenetic analysis of the genus Pectinidens (Gastropoda)

Freshwater lymnaeid snails are crucial in defining transmission and epidemiology of fascioliasis. In South America, human endemic areas are related to high altitudes in Andean regions. The species Lymnaea diaphana has, however, been involved in low altitude areas of Chile, Argentina and Peru where human infection also occurs. Complete nuclear ribosomal DNA 18S, internal transcribed spacer (ITS)-2 and ITS-1 and fragments of mitochondrial DNA 16S and cytochrome c oxidase (cox)1 genes of L. diaphana specimens from its type locality offered 1,848, 495, 520, 424 and 672 bp long sequences. Comparisons with New and Old World Galba/Fossaria, Palaearctic stagnicolines, Nearctic stagnicolines, Old World Radix and Pseudosuccinea allowed to conclude that (i) L. diaphana shows sequences very different from all other lymnaeids, (ii) each marker allows its differentiation, except cox1 amino acid sequence, and (iii) L. diaphana is not a fossarine lymnaeid, but rather an archaic relict form derived from the oldest North American stagnicoline ancestors. Phylogeny and large genetic distances support the genus Pectinidens as the first stagnicoline representative in the southern hemisphere, including colonization of extreme world regions, as most southern Patagonia, long time ago. The phylogenetic link of L. diaphana with the stagnicoline group may give light to the aforementioned peculiar low altitude epidemiological scenario of fascioliasis.

Risk analysis of climate change on coffee nematodes and leaf miner in Brazil

The objective of this work was to assess the potential impact of climate change on the spatial distribution of coffee nematodes (races of Meloidogyne incognita) and leaf miner (Leucoptera coffeella), using a Geographic Information System. Assessment of the impacts of climate change on pest infestations and disease epidemics in crops is needed as a basis for revising management practices to minimize crop losses as climatic conditions shift. Future scenarios focused on the decades of the 2020's, 2050's, and 2080's (scenarios A2 and B2) were obtained from five General Circulation Models available on Data Distribution Centre from Intergovernmental Panel on Climate Change. Geographic distribution maps were prepared using models to predict the number of generations of the nematodes and leaf miner. Maps obtained in scenario A2 allowed prediction of an increased infestation of the nematode and of the pest, due to greater number of generations per month, than occurred under the climatological normal from 1961-1990. The number of generations also increased in the B2 scenario, but was lower than in the A2 scenario for both organisms.

An analysis of the risk of cocoa moniliasis occurrence in Brazil as the result of climate change

The aim of this study was to evaluate the potential risk of moniliasis occurrence and the impacts of climate change on this disease in the coming decades, should this pathogen be introduced in Brazil. To this end, climate favorability maps were devised for the occurrence of moniliasis, both for the present and future time. The future scenarios (A2 and B2) focused on the decades of 2020, 2050 and 2080. These scenarios were obtained from six global climate models (GCMs) made available by the third assessment report of Intergovernmental Panel on Climate Change (IPCC). Currently, there are large areas with favorable climate conditions for moniliasis in Brazil, especially in regions at high risk of introduction of that pathogen. Considering the global warming scenarios provided by the IPCC, the potential risk of moniliasis occurrence in Brazil will be reduced. This decrease is predicted for both future scenarios, but will occur more sharply in scenario A2. However, there will still be areas with favorable climate conditions for the development of the disease, particularly in Brazil's main producing regions. Moreover, pathogen and host alike may undergo alterations due to climate change, which will affect the extent of their impacts on this pathosystem.

Sensibility analysis of the pearl model for pesticide leaching in the State of Mato Grosso do Sul, Brazil

For an accurate use of pesticide leaching models it is necessary to assess the sensitivity of input parameters. The aim of this work was to carry out sensitivity analysis of the pesticide leaching model PEARL for contrasting soil types of Dourados river watershed in the state of Mato Grosso do Sul, Brazil. Sensitivity analysis was done by carrying out many simulations with different input parameters and calculating their influence on the output values. The approach used was called one-at-a-time sensitivity analysis, which consists in varying independently input parameters one at a time and keeping all others constant with the standard scenario. Sensitivity analysis was automated using SESAN tool that was linked to the PEARL model. Results have shown that only soil characteristics influenced the simulated water flux resulting in none variation of this variable for scenarios with different pesticides and same soil. All input parameters that showed the greatest sensitivity with regard to leached pesticide are related to soil and pesticide properties. Sensitivity of all input parameters was scenario dependent, confirming the need of using more than one standard scenario for sensitivity analysis of pesticide leaching models.