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 to other positive variables of interest beyond the time domain. 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 Niño/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 predictor

Sensitivity of APSIM/ORYZA model due to estimation errors in solar radiation

Crop models are ideally suited to quantify existing climatic risks. However, they require historic climate data as input. While daily temperature and rainfall data are often available, the lack of observed solar radiation (Rs) data severely limits site-specific crop modelling. The objective of this study was to estimate Rs based on air temperature solar radiation models and to quantify the propagation of errors in simulated radiation on several APSIM/ORYZA crop model seasonal outputs, yield, biomass, leaf area (LAI) and total accumulated solar radiation (SRA) during the crop cycle. The accuracy of the 5 models for estimated daily solar radiation was similar, and it was not substantially different among sites. For water limited environments (no irrigation), crop model outputs yield, biomass and LAI was not sensitive for the uncertainties in radiation models studied here

Biochar improves fertility of a clay soil in the Brazilian Savannah: short term effects and impact on rice yield

The objective of this study was to report single season effects of wood biochar (char) application coupled with N fertilization on soil chemical properties, aerobic rice growth and grain yield in a clayey Rhodic Ferralsol in the Brazilian Savannah. Char application effected an increase in soil pH, K, Ca, Mg, CEC, Mn and nitrate while decreasing Al content and potential acidity of soils. No distinct effect of char application on grain yield of aerobic rice was observed. We believe that soil properties impacted by char application were inconsequential for rice yields because neither water, low pH, nor the availability of K or P were limiting factors for rice production. Rate of char above 16 Mgha -1 reduced leaf area index and total shoot dry matter by 72 days after sowing. The number of panicles infected by rice blast decreased with increasing char rate. Increased dry matter beyond the remobilization capacity of the crop, and high number of panicles infected by rice blast were the likely cause of the lower grain yield observed when more than 60 kgNha-1 was applied. The optimal rate of N was 46 kg ha-1 and resulted in a rice grain yield above 3Mgha-1