Methodology for climate risk assessment of maize intercropped with Brachiaria in the Cerrado of Brazil.

2015

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

Panicum maximum cv. Tanzânia: climate trends and regional pasture production in Brazil.

Projected change in forage production under a range of climate scenarios is important for the evaluation of the impacts of global climate change on pasture-based livestock production systems in Brazil. We evaluated the effects of regional climate trends on Panicum maximum cv. Tanzânia production, predicted by agro-meteorological model considering the sum of degree days and corrected by a water availa bility index. Data from Brazilian weather stations (1963?2009) were considered as the current climate (baseline), and future scenarios, based on contrasting scenarios interms of increased temperature and atmospheric CO2 concentrations (high and low increases), were determined for 2013?2040 (2025 scenario) and for 2043?2070 (2055 scenario). Predicted baseline scenarios indicated that there are regional and seasonal variations in P. maximum production related to variation intemperature and water availability during the year. Production was lower in the Northeast region and higher in the rainforest area. Total annual productionunder future climate scenarios was predicted toincrease by up to 20% for most of the Brazilian area, mainly due to temperature increase, according to each climate model and scenario evaluated. The highest increase in forage production is expected to be in the South, Southeast and Central-west areas of Brazil. In these regions, future climate scenarios will not lead to changes in the seasonal production, with largerincreases in productivity during the summer. Climate risk is expected to decrease, as the probability of occurrence of low forage productions will be lower. Due to the predicted increase in temperature and decrease in rainfall in the Northeast area, P. maximum production is expected to decrease, mainly when considering scenarios based on the PRECIS model for the 2055 scenario.

Approaches to assess climate change effects on plant disease: (2) mapping risk in Brazil.

2008

Risk analysis of climate change on black sigatoka in banana in Brazil.

2008

Assessing the risk of the evolution of resistance to pesticides using spatially complex simulation models.

2008

Sugarcane herbicide leaching risk evaluation in Guarany Aquifer.

he region of Ribeirão Preto, São Paulo State, Brazil, is located over recharge area of the Guarany aquifer, the most important source of groundwater in the South Central region of the country. This region is also the most important sugarcane producing area of the country which produces a large amount of the ethanol. This study was conducted to determine the potential risk of herbicide groundwater contamination. The leaching risk potential of herbicides to groundwater was conducted using the weather simulator ?Weather Generator? (WGEN) coupled with the model ?Chemical Movement Trough Layered Soils? (CMLS94). The following herbicides were evaluated in clayey and sandy soils (Typic Haplorthox and Typic Quartzipsamment soils) found in the region: ametryn (N-ethyl-N\'-(1- methylethyl)-6-(methylthio)-1,3,5-triazine-2,4-diamine), atrazine (6-chloro-N-ethyl-N\'-(1-methylethyl)-1,3,5-triazine- 2,4-diamine), clomazone (2-[(2-chlorophenyl)methyl]-4,4-dimethyl-3-isoxazolidinone), diuron (3,4-dichlorophenyl)- N,N-dimethylurea), halosulfuron (3-chloro-5-[(4,6-dimethoxy-2-pyrimidinyl)amino]carbonyl], hexazinone (3- cyclohexyl-6-(dimethylamino)-1-methyl-1,3,5-triazine-2,4 (1H,3H)-dione), imazapic ((±)-2-[4,5-dihydro-4-methyl-4- (1-methylethyl)-5-oxo-1H-imidazol-2-yl]-5-methyl-3-pyridinecarboxylic acid), imazapyr ((±)-2-[4,5-dihydro-4-methyl- 4-(1-methylethyl)-5-oxo-1H-imidazol-2-yl]-3-pyridinecarboxylic acid), MCPA (4-chloro-2-methylphenoxy)acetic acid), metribuzin (4-amino-6-(1,1-dimethylethyl)-3-(methylthio)-1,2,4-triazin-5(4H)-one), MSMA (Amonosodium salt of MAA), paraquat (1,1\'-dimethyl-4,4\'-bipyridinium ion), pendimethalin (N-(1-ethylpropyl)-3,4-dimethyl-2,6- dinitrobenzenamine), picloram (4-amino-3,5,6-trichloro-2-pyridinecarboxylic acid), simazine (6-chloro-N,N\'-diethyl- 1,3,5-triazine-2,4-diamine), sulfentrazone [N-[2,4-dichloro-5-[4-(difluoromethyl)-4,5-dihydro-3-methyl-5-oxo-1H- 1,2,4-triazol-1-yl]phenyl]methanesulfonamide], and tebuthiuron [N-[5-(1,1-dimethylethyl)-1,3,4-thiadiazol-2-yl]-N,N\'- dimethylurea]. Results obtained by our simulation study have shown that the herbicides picloram, tebuthiuron, and metribuzin have the highest leaching potential, in either sandy or clayey soils, with picloram reaching the root zone of sugarcane at 0.6m in less than 150 days.