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The main objective of this study was to assess the impact of biochar rate (0, 8, 16 and 32 Mg ha-1) on the water retention capacity (WRC) of a sandy loam Dystric Plinthosol. The applied biochar was a by-product of slow pyrolysis (~450 °C) of eucalyptus wood, milled to pass through a 2000 µm sieve that resulted in a material with an intrinsic porosity =10 µm and a specific surface area of ~3.2 m2 g-1. The biochar was incorporated into the top 15 cm of the soil under an aerobic rice system. Our study focused on both the effects on WRC and rice yields 2 and 3 years after its application. Undisturbed soil samples were collected from 16 plots in two soil layers (5–10 and 15–20 cm). Soil water retention curves were modelled using a nonlinear mixed model which appropriately accounts for uncertainties inherent of spatial variability and repeated measurements taken within a specific soil sample. We found an increase in plant-available water in the upper soil layer proportional to the rate of biochar, with about 0.8% for each Mg ha-1 biochar amendment 2 and 3 years after its application. The impact of biochar on soil WRC was most likely related to an effect in overall porosity of the sandy loam soil, which was evident from an increase in saturated soil moisture and macro porosity with 0.5 and 1.6% for each Mg ha-1 of biochar applied, respectively. The increment in soil WRC did not translate into an increase in rice yield, essentially because in both seasons the amount of rainfall during the critical period for rice production exceeded 650 mm. The use of biochar as a soil amendment can be a worthy strategy to guarantee yield stability under short-term water-limited conditions. Our findings raise the importance of assessing the feasibility of very high application rates of biochar and the inclusion of a detailed analysis of its physical and chemical properties as part of future investigations.

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.

This paper discusses the key information needs to reduce the negative impacts of weather variability and climate change on land degradation and food security, and identifies the opportunities and barriers between the information and services needed. It suggests that vulnerability assessments based on a livelihood concept that includes climate information and key socio-economic variables can overcome the narrow focus of common one-dimensional vulnerability studies. Both current and future climatic risks can be managed better if there is appropriate policy and institutional support together with technological interventions to address the complexities of multiple risks that agriculture has to face. This would require effective partnerships among agencies dealing with meteorological and hydrological services, agricultural research, land degradation and food security issues. In addition a state-of-the-art infrastructure to measure, record, store and disseminate data on weather variables, and access to weather and seasonal climate forecasts at desired spatial and temporal scales would be needed.

Much has been learned in the interpretation and use of climate information since the 1997/1998 El Niño event that garnered so much attention. Seasonal-to-interannual forecasts are now produced around the world. However, mismatches in their scales, specificity or communication (of forecast content and uncertainties) with decision-maker needs still hinder their use. More work is needed to improve a) the utility of models, b) access to observational and model/forecast data, c) understanding and communication of the opportunities and limitations of forecasts, and d) methods by which decision systems use climate predictions – both through modifications of decision systems and more tailored forecast information. This white paper discusses these issues and recent advances in providing climate information needed in effective climate risk management. The paper advocates for the establishment and/or strengthening of “chains of experts and communications” to better enable appropriate and effective dissemination, assimilation and further use and application of climate information

Impacts of the Madden¿Julian oscillation (MJO) on Australian rainfall and circulation are examined during all four seasons. The authors examine circulation anomalies and a number of different rainfall metrics, each composited contemporaneously for eight MJO phases derived from the real-time multivariate MJO index. Multiple rainfall metrics are examined to allow for greater relevance of the information for applications. The greatest rainfall impact of the MJO occurs in northern Australia in (austral) summer, although in every season rainfall impacts of various magnitude are found in most locations, associated with corresponding circulation anomalies. In northern Australia in all seasons except winter, the rainfall impact is explained by the direct influence of the MJO's tropical convective anomalies, while in winter a weaker and more localized signal in northern Australia appears to result from the modulation of the trade winds as they impinge upon the eastern coasts, especially in the northeast. In extratropical Australia, on the other hand, the occurrence of enhanced (suppressed) rainfall appears to result from induced upward (downward) motion within remotely forced extratropical lows (highs), and from anomalous low-level northerly (southerly) winds that transport moisture from the tropics. Induction of extratropical rainfall anomalies by remotely forced lows and highs appears to operate mostly in winter, whereas anomalous meridional moisture transport appears to operate mainly in the summer, autumn, and to some extent in the spring

Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of ¿quality.¿ However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels (p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal¿Wallis (KW) or Kolmogorov¿Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linear error in the probability space (LEPS) and ranked probability skill score (RPSS)] to the five-phase Southern Oscillation index classification system using historical rainfall data from Australia, South Africa, and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. It is found that nonparametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of d

The strong trends in climate change already evident, the likelihood of further changes occurring, and the increasing scale of potential climate impacts give urgency to addressing agricultural adaptation more coherently. There are many potential adaptation options available for marginal change of existing agricultural systems, often variations of existing climate risk management. We show that implementation of these options is likely to have substantial benefits under moderate climate change for some cropping systems. However, there are limits to their effectiveness under more severe climate changes. Hence, more systemic changes in resource allocation need to be considered, such as targeted diversification of production systems and livelihoods. We argue that achieving increased adaptation action will necessitate integration of climate change-related issues with other risk factors, such as climate variability and market risk, and with other policy domains, such as sustainable development. Dealing with the many barriers to effective adaptation will require a comprehensive and dynamic policy approach covering a range of scales and issues, for example, from the understanding by farmers of change in risk profiles to the establishment of efficient markets that facilitate response strategies. Science, too, has to adapt. Multidisciplinary problems require multidisciplinary solutions, i.e., a focus on integrated rather than disciplinary science and a strengthening of the interface with decision makers. A crucial component of this approach is the implementation of adaptation assessment frameworks that are relevant, robust, and easily operated by all stakeholders, practitioners, policymakers, and scientists.

The amount and timing of early wet-season rainfall are important for the management of many agricultural industries in north Australia. With this in mind, a wet-season onset date is defined based on the accumulation of rainfall to a predefined threshold, starting from 1 September, for each square of a 1° gridded analysis of daily rainfall across the region. Consistent with earlier studies, the interannual variability of the onset dates is shown to be well related to the immediately preceding July¿August Southern Oscillation index (SOI). Based on this relationship, a forecast method using logistic regression is developed to predict the probability that onset will occur later than the climatological mean date. This method is expanded to also predict the probabilities that onset will be later than any of a range of threshold dates around the climatological mean. When assessed using cross-validated hindcasts, the skill of the predictions exceeds that of climatological forecasts in the majority of locations in north Australia, especially in the Top End region, Cape York, and central Queensland. At times of strong anomalies in the July¿August SOI, the forecasts are reliably emphatic. Furthermore, predictions using tropical Pacific sea surface temperatures (SSTs) as the predictor are also tested. While short-lead (July¿August predictor) forecasts are more skillful using the SOI, long-lead (May¿June predictor) forecasts are more skillful using Pacific SSTs, indicative of the longer-term memory present in the ocean.

The traditional reductionist approach to science has a tendency to create `islands of knowledge in a sea of ignorance¿, with a much stronger focus on analysis of scientific inputs rather than synthesis of socially relevant outcomes. This might be the principal reason why intended end users of climate information generally fail to embrace what the climate science community has to offer. The translation of climate information into real-life action requires 3 essential components: salience (the perceived relevance of the information), credibility (the perceived technical quality of the information) and legitimacy (the perceived objectivity of the process by which the information is shared). We explore each of these components using 3 case studies focused on dryland cropping in Australia, India and Brazil. In regards to `salience¿ we discuss the challenge for climate science to be `policy-relevant¿, using Australian drought policy as an example. In a village in southern India `credibility¿ was gained through engagement between scientists and risk managers with the aim of building social capital, achieved only at high cost to science institutions. Finally, in Brazil we found that `legitimacy¿ is a fragile, yet renewable resource that needs to be part of the package for successful climate applications; legitimacy can be easily eroded but is difficult to recover. We conclude that climate risk management requires holistic solutions derived from cross-disciplinary and participatory, user-oriented research. Approaches that combine climate, agroecological and socioeconomic models provide the scientific capabilities for establishment of `borderless¿ institutions without disciplinary constraints. Such institutions could provide the necessary support and flexibility to deliver the social benefits of climate science across diverse contexts. Our case studies show that this type of solution is already being applied, and suggest that the climate science community attempt to address e